Skip to content

HTTPS clone URL

Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP
branch: master
Fetching contributors…

Cannot retrieve contributors at this time

2292 lines (2221 sloc) 120.171 kb
% würde ich gerne lesen
@article {springerlink:10.1007/s11721-010-0043-7,
author = {Torres, Claudio and Rossi, Louis and Keffer, Jeremy and Li, Ke and Shen, Chien-Chung},
affiliation = {University of Delaware Department of Mathematical Sciences Newark DE 19716 USA},
title = {Modeling, analysis and simulation of ant-based network routing protocols},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {221-244},
volume = {4},
issue = {3},
url = {http://dx.doi.org/10.1007/s11721-010-0043-7},
note = {10.1007/s11721-010-0043-7},
year = {2010}
}
% References of face detection
@article{mobahi2006swarm,
title={Swarm contours: A fast self-organization approach for snake initialization},
author={Mobahi, H. and Ahmadabadi, M.N. and Araabi, B.N.},
journal={Complexity},
volume={12},
number={1},
pages={41--52},
year={2006},
publisher={Wiley Online Library}
}
% überblick
@article{kennedy2006swarm,
title={Swarm intelligence},
author={Kennedy, J.},
journal={Handbook of Nature-Inspired and Innovative Computing},
pages={187--219},
year={2006},
publisher={Springer}
}
% Swarmintelligence
@PHDTHESIS{Diplom.Thiem,
author = {Stefanie Thiem},
title = {Swarmintelligence - Simulation, Optimization and Comparative Analysis},
school = {Chemnitz University of Technology},
month = {January},
year = {2008},
type = {Diplom Thesis}
}
% from springer journal "swarm intelligence"
% wahrscheinlichkeitsüberprüfung
@article {springerlink:10.1007/s11721-009-0037-5,
author = {El-Abd, Mohammed and Kamel, Mohamed},
affiliation = {ECE Department, University of Waterloo, 200 University Av. W., Waterloo, Ontario N2L3G1, Canada},
title = {A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {57-89},
volume = {4},
issue = {1},
url = {http://dx.doi.org/10.1007/s11721-009-0037-5},
note = {10.1007/s11721-009-0037-5},
year = {2010}
}
$ technischer Ansatz
@article {springerlink:10.1007/s11721-011-0053-0,
author = {Ducatelle, Frederick and Di Caro, Gianni and Pinciroli, Carlo and Gambardella, Luca},
affiliation = {“Dalle Molle” Institute for Artificial Intelligence Studies (IDSIA), Galleria 2, 6928 Manno, Switzerland},
title = {Self-organized cooperation between robotic swarms},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {73-96},
volume = {5},
issue = {2},
url = {http://dx.doi.org/10.1007/s11721-011-0053-0},
note = {10.1007/s11721-011-0053-0},
year = {2011}
}
% Prozessorientirtes Optimieren
@article {springerlink:10.1007/s11721-011-0061-0,
author = {Pellegrini, Paola and Stützle, Thomas and Birattari, Mauro},
affiliation = {IRIDIA, CoDE, Université Libre de Bruxelles (ULB), Brussels, Belgium},
title = {A critical analysis of parameter adaptation in ant colony optimization},
journal = {Swarm Intelligence},
publisher = {Springer New York},
issn = {1935-3812},
keyword = {Technik},
pages = {1-26},
url = {http://dx.doi.org/10.1007/s11721-011-0061-0},
note = {10.1007/s11721-011-0061-0},
}
% general Overview
@INPROCEEDINGS{5623005,
author={Yan-fei Zhu and Xiong-min Tang},
booktitle={Computer Application and System Modeling (ICCASM), 2010 International Conference on},
title={Overview of swarm intelligence},
year={2010},
month={oct.},
volume={9},
number={},
pages={V9-400 -V9-403},
keywords={Kazadi two-step process;artificial literature;biological basis;biologists;naturalists;operational principle;robot system;swarm engineering;swarm intelligence;artificial intelligence;particle swarm optimisation;},
doi={10.1109/ICCASM.2010.5623005},
ISSN={},}
% physical application
@INPROCEEDINGS{5760115,
author={Affijulla, S. and Chauhan, S.},
booktitle={Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on},
title={Swarm intelligence solution to large scale thermal power plant Load Dispatch},
year={2011},
month={march},
volume={},
number={},
pages={196 -199},
keywords={artificial intelligence;economic load dispatch;energy management system;evolutionary computation;evolutionary programming;generating units;genetic algorithm;nonconvex objective functions;nondifferential objective functions;optimal real power settings;particle swarm optimization;power generation;swarm intelligence solution;thermal power plant;artificial intelligence;energy management systems;evolutionary computation;particle swarm optimisation;power generation dispatch;thermal power stations;},
doi={10.1109/ICETECT.2011.5760115},
ISSN={},}
@book{engelbrecht2005fundamentals,
title={Fundamentals of computational swarm intelligence},
author={Engelbrecht, A.P.},
volume={1},
year={2005},
publisher={Wiley Chichester,, UK}
}
% collective intelligence; 672 zitationen; grundlagen werk
@article{DBLP:journals/corr/cs-LG-9908014,
author = {David Wolpert and
Kagan Tumer},
title = {An Introduction to Collective Intelligence},
journal = {CoRR},
volume = {cs.LG/9908014},
year = {1999},
ee = {http://arxiv.org/abs/cs.LG/9908014},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
% collective intelligence particle physics
@article{lykourentzou2011collective,
title={Collective Intelligence Systems: Classification and Modeling},
author={Lykourentzou, I. and Vergados, D.J. and Kapetanios, E. and Loumos, V.},
journal={Journal of Emerging Technologies in Web Intelligence},
volume={3},
number={3},
pages={217--226},
year={2011}
}
% Datenbanksuche, Collective Intelligence
@inproceedings{zhu2010overview,
title={Overview of swarm intelligence},
author={Zhu, Y. and Tang, X.},
booktitle={Computer Application and System Modeling (ICCASM), 2010 International Conference on},
volume={9},
pages={V9--400},
year={2010},
organization={IEEE}
}
% Datenbanksuche, Swarm Intelligence
@inproceedings{sun2004global,
title={A global search strategy of quantum-behaved particle swarm optimization},
author={Sun, J. and Xu, W. and Feng, B.},
booktitle={Cybernetics and Intelligent Systems, 2004 IEEE Conference on},
volume={1},
pages={111--116},
year={2004},
organization={IEEE}
}
% quantum-behave particle swarm optimization
@article{du2010improved,
title={Improved Quantum Particle Swarm Optimization by Bloch Sphere},
author={Du, Y. and Duan, H. and Liao, R. and Li, X.},
journal={Advances in Swarm Intelligence},
pages={135--143},
year={2010},
publisher={Springer}
}
% swarm intelligence, quantum
@inproceedings{affijulla2011swarm,
title={Swarm intelligence solution to large scale thermal power plant Load Dispatch},
author={Affijulla, S. and Chauhan, S.},
booktitle={Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on},
pages={196--199},
year={2011},
organization={IEEE}
}
% swarm intelligence, power plant; 25 zitationen
@article{coelho2008solving,
title={Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches},
author={Coelho, L.S. and Lee, C.S.},
journal={International Journal of Electrical Power \& Energy Systems},
volume={30},
number={5},
pages={297--307},
year={2008},
publisher={Elsevier}
}
% swarm intelligence, load dispatch; 62 zitationen
% kommt in frage zum lesen
@inproceedings{el2001swarm,
title={Swarm intelligence for hybrid cost dispatch problem},
author={El-Gallad, AI and El-Hawary, M. and Sallam, AA and Kalas, A.},
booktitle={Electrical and Computer Engineering, 2001. Canadian Conference on},
volume={2},
pages={753--757},
year={2001},
organization={IEEE}
}
% swarm intelligence, load dispatch; 22 zitationen
%w\"urde ich gern lesen
@article{selvakumar2007new,
title={A new particle swarm optimization solution to nonconvex economic dispatch problems},
author={Selvakumar, A.I. and Thanushkodi, K.},
journal={Power Systems, IEEE Transactions on},
volume={22},
number={1},
pages={42--51},
year={2007},
publisher={IEEE}
}
% swarm intelligence, load dispatch; 167 zitationen
% This file was created with JabRef 2.7b.
% Encoding: UTF-8
@ARTICLE{bib:pso_pid_gaing,
author = {Zwe-Lee Gaing},
title = {A particle swarm optimization approach for optimum design of PID
controller in AVR system},
journal = {Energy Conversion, IEEE Transactions on},
year = {2004},
volume = {19},
pages = {384 - 391},
number = {2},
month = {june},
doi = {10.1109/TEC.2003.821821},
}
abstract = { In this paper, a novel design method for determining the optimal
proportional-integral-derivative (PID) controller parameters of an
AVR system using the particle swarm optimization (PSO) algorithm
is presented. This paper demonstrated in detail how to employ the
PSO method to search efficiently the optimal PID controller parameters
of an AVR system. The proposed approach had superior features, including
easy implementation, stable convergence characteristic, and good
computational efficiency. Fast tuning of optimum PID controller parameters
yields high-quality solution. In order to assist estimating the performance
of the proposed PSO-PID controller, a new time-domain performance
criterion function was also defined. Compared with the genetic algorithm
(GA), the proposed method was indeed more efficient and robust in
improving the step response of an AVR system.},
doi = {10.1109/TEC.2003.821821},
issn = {0885-8969},
keywords = { AVR system; PID controller parameter; genetic algorithm; particle
swarm optimisation algorithm; optimisation; three-term control; voltage
regulators;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@INPROCEEDINGS{bib:pso_kennedy,
author = {Kennedy, J. and Eberhart, R.},
title = {Particle swarm optimization},
booktitle = {Neural Networks, 1995. Proceedings., IEEE International Conference
on},
year = {1995},
volume = {4},
pages = {1942 -1948 vol.4},
month = {nov/dec},
note = {Zitiert durch: 15780
Begruender von PSO},
abstract = {A concept for the optimization of nonlinear functions using particle
swarm methodology is introduced. The evolution of several paradigms
is outlined, and an implementation of one of the paradigms is discussed.
Benchmark testing of the paradigm is described, and applications,
including nonlinear function optimization and neural network training,
are proposed. The relationships between particle swarm optimization
and both artificial life and genetic algorithms are described},
doi = {10.1109/ICNN.1995.488968},
keywords = {artificial life;evolution;genetic algorithms;multidimensional search;neural
network;nonlinear functions;optimization;particle swarm;simulation;social
metaphor;artificial intelligence;genetic algorithms;neural nets;search
problems;simulation;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@ARTICLE{bib:pso_wsn_kulkarni,
author = {Kulkarni, R.V. and Venayagamoorthy, G.K.},
title = {Particle Swarm Optimization in Wireless-Sensor Networks: A Brief
Survey},
journal = {Systems, Man, and Cybernetics, Part C: Applications and Reviews,
IEEE Transactions on},
year = {2011},
volume = {41},
pages = {262 -267},
number = {2},
month = {march},
abstract = {Wireless-sensor networks (WSNs) are networks of autonomous nodes used
for monitoring an environment. Developers of WSNs face challenges
that arise from communication link failures, memory and computational
constraints, and limited energy. Many issues in WSNs are formulated
as multidimensional optimization problems, and approached through
bioinspired techniques. Particle swarm optimization (PSO) is a simple,
effective, and computationally efficient optimization algorithm.
It has been applied to address WSN issues such as optimal deployment,
node localization, clustering, and data aggregation. This paper outlines
issues in WSNs, introduces PSO, and discusses its suitability for
WSN applications. It also presents a brief survey of how PSO is tailored
to address these issues.},
doi = {10.1109/TSMCC.2010.2054080},
issn = {1094-6977},
keywords = {clustering issue;data aggregation issue;node localization issue;optimal
deployment issue;particle swarm optimization;wireless sensor networks;particle
swarm optimisation;wireless sensor networks;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@INPROCEEDINGS{bib:pso_energy_wsn_latiff,
author = {Latiff, N.M.A. and Tsimenidis, C.C. and Sharif, B.S.},
title = {Energy-Aware Clustering for Wireless Sensor Networks using Particle
Swarm Optimization},
booktitle = {Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007.
IEEE 18th International Symposium on},
year = {2007},
pages = {1 -5},
month = {sept.},
abstract = {Wireless sensor networks (WSNs) are mainly characterized by their
limited and non-replenishable energy supply. Hence, the need for
energy efficient infrastructure is becoming increasingly more important
since it impacts upon the network operational lifetime. Sensor node
clustering is one of the techniques that can expand the lifespan
of the whole network through data aggregation at the cluster head.
In this paper, we present an energy-aware clustering for wireless
sensor networks using particle swarm optimization (PSO) algorithm
which is implemented at the base station. We define a new cost function,
with the objective of simultaneously minimizing the intra-cluster
distance and optimizing the energy consumption of the network. The
performance of our protocol is compared with the well known cluster-based
protocol developed for WSNs, LEACH (low-energy adaptive clustering
hierarchy) and LEACH-C, the later being an improved version of LEACH.
Simulation results demonstrate that our proposed protocol can achieve
better network lifetime and data delivery at the base station over
its comparatives.},
doi = {10.1109/PIMRC.2007.4394521},
keywords = {LEACH;LEACH-C;WSN;cluster-based protocol;cost function;data aggregation;data
delivery;energy consumption;energy efficient infrastructure;energy-aware
clustering;intra-cluster distance;low-energy adaptive clustering
hierarchy;network operational lifetime;nonreplenishable energy supply;particle
swarm optimization;sensor node clustering;wireless sensor networks;particle
swarm optimisation;protocols;wireless sensor networks;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@ARTICLE{bib:pso_electromagn_robinson,
author = {Robinson, J. and Rahmat-Samii, Y.},
title = {Particle swarm optimization in electromagnetics},
journal = {Antennas and Propagation, IEEE Transactions on},
year = {2004},
volume = {52},
pages = {397 - 407},
number = {2},
month = {feb.},
abstract = {The particle swarm optimization (PSO), new to the electromagnetics
community, is a robust stochastic evolutionary computation technique
based on the movement and intelligence of swarms. This paper introduces
a conceptual overview and detailed explanation of the PSO algorithm,
as well as how it can be used for electromagnetic optimizations.
This paper also presents several results illustrating the swarm behavior
in a PSO algorithm developed by the authors at UCLA specifically
for engineering optimizations (UCLA-PSO). Also discussed is recent
progress in the development of the PSO and the special considerations
needed for engineering implementation including suggestions for the
selection of parameter values. Additionally, a study of boundary
conditions is presented indicating the invisible wall technique outperforms
absorbing and reflecting wall techniques. These concepts are then
integrated into a representative example of optimization of a profiled
corrugated horn antenna.},
doi = {10.1109/TAP.2004.823969},
issn = {0018-926X},
keywords = { UCLA; antenna design; corrugated horn antenna; electromagnetics;
genetic algorithm; invisible wall technique; particle swarm optimization;
stochastic evolutionary computation technique; antenna theory; electromagnetism;
evolutionary computation; genetic algorithms; horn antennas;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@ARTICLE{bib:pso_methods_sedighizadeh,
author = {Sedighizadeh, D. and Masehian, E.},
title = {Particle Swarm Optimization Methods, Taxonomy and Applications},
journal = {International Journal of Computer Theory and Engineering},
year = {2009},
volume = {1},
pages = {1793--8201},
number = {5},
owner = {cornelius},
timestamp = {2011.11.26}
}
@INPROCEEDINGS{bib:salesman_wang,
author = {Kang-Ping Wang and Lan Huang and Chun-Guang Zhou and Wei Pang},
title = {Particle swarm optimization for traveling salesman problem},
booktitle = {Machine Learning and Cybernetics, 2003 International Conference on},
year = {2003},
volume = {3},
pages = { 1583 - 1585 Vol.3},
month = {nov.},
abstract = { This paper proposes a new application of particle swarm optimization
for traveling salesman problem. We have developed some special methods
for solving TSP using PSO. We have also proposed the concept of swap
operator and swap sequence, and redefined some operators on the basis
of them, in this way the paper has designed a special PSO. The experiments
show that it can achieve good results.},
doi = {10.1109/ICMLC.2003.1259748},
keywords = { particle swarm optimization; swap operator; swap sequence; traveling
salesman problem; genetic algorithms; travelling salesman problems;},
owner = {cornelius},
timestamp = {2011.11.26}
}
@comment{jabref-meta: selector_publisher:}
@comment{jabref-meta: selector_author:}
@comment{jabref-meta: selector_journal:}
@comment{jabref-meta: selector_keywords:}
%% überblick über verschiedene Algorithmen
%% favorisierte Quelle!
%% http://www.springerlink.com/content/mt70684828340818/
@incollection {springerlink:10.1007/978-3-642-23935-9_3,
author = {Chu, Shu-Chuan and Huang, Hsiang-Cheh and Roddick, John and Pan, Jeng-Shyang},
affiliation = {School of Computer Science, Engineering and Mathematics, Flinders University of South Australia, Australia},
title = {Overview of Algorithms for Swarm Intelligence},
booktitle = {Computational Collective Intelligence. Technologies and Applications},
series = {Lecture Notes in Computer Science},
editor = {Jedrzejowicz, Piotr and Nguyen, Ngoc and Hoang, Kiem},
publisher = {Springer Berlin / Heidelberg},
isbn = {978-3-642-23934-2},
keyword = {Computer Science},
pages = {28-41},
volume = {6922},
url = {http://dx.doi.org/10.1007/978-3-642-23935-9_3},
note = {10.1007/978-3-642-23935-9_3},
year = {2011}
}
%%googlescholar "swarm intelligence"
%%allgemein über das thema
@book{shi2011handbook,
title={Handbook of Swarm Intelligence: Concepts, Principles and Applications},
author={Shi, Y.},
volume={8},
year={2011},
publisher={Springer Verlag}
}
%% google bücher: Künstlichen Intelligenz
%% allgemin, robotik, softwareagent
@book{görz2003handbuch,
title={Handbuch der k{\"u}nstlichen Intelligenz},
author={G{\"o}rz, G.},
year={2003},
publisher={Oldenbourg Wissenschaftsverlag}
}
%%scholar allintitle: algorithms swarm intelligence
%% Algorithms for Data Clustering
%% zitiert durch 42
@incollection {springerlink:10.1007/978-0-387-69935-6_12,
author = {Abraham, Ajith and Das, Swagatam and Roy, Sandip},
affiliation = {Norwegian University of Science and Technology Center of excellence for Quanti¯able Quality of Service (Q2S) Trondheim Norway},
title = {Swarm Intelligence Algorithms for Data Clustering},
booktitle = {Soft Computing for Knowledge Discovery and Data Mining},
editor = {Maimon, Oded and Rokach, Lior},
publisher = {Springer US},
isbn = {978-0-387-69935-6},
keyword = {Computer Science},
pages = {279-313},
url = {http://dx.doi.org/10.1007/978-0-387-69935-6_12},
note = {10.1007/978-0-387-69935-6_12},
year = {2008}
}%Suchen auf Deutsch
% Begriff "Schwarmintelligenz Physik und Informatik" bei google eingeben --> vorallem Bachelorarbeiten und Diplomarbeiten als Ergebnisse
%Begriffe "intitle:Schwarmintelligenz" und "filetype_pdf intitle:Schwarmintelligenz" bei google eingeben
%google Scholar verwenden
%Suchen auf Englisch
%Begriffe "swarm intelligence" oder "collective intelligence" bei Google und Google Scholar eingeben; wenn englischer Name nicht bekannt ist, dann bei Wikipedia schauen und Sprache der Wahl anklicken
%Unterbegriffe "swarm intelligence literature overview" und "swarm intelligence thesis" bei google eingeben
%%Bücher
%zitiert von 219 bei Google Scholar
%alle Angaben beim Eintrag vollständig, bis auf die empfehlenswerte Angabe address
@book{segaran2007programming,
title={Programming collective intelligence: building smart web 2.0 applications},
author={Segaran, T.},
year={2007},
publisher={O'Reilly Media}
}
%zitiert von 672 bei Google Scholar
%alle Angaben beim Eintrag vollständig, bis auf die empfehlenswerte Angabe address
%Anmerkung: eher ein Übersichtsbuch, sollte bei der endgültigen Version der Arbeit vllt. nicht zitiert werden
@book{engelbrecht2005fundamentals,
title={Fundamentals of computational swarm intelligence},
author={Engelbrecht, A.P.},
year={2005},
publisher={Wiley Chichester,, UK}
}
%%Wissenschaftliche Artikel
%zitiert durch 84 bei Google Scholar
%alle Angaben beim Eintrag vollständig, bis auf die empfehlenswerte Angabe issue/number
@article{garnier2007biological,
title={The biological principles of swarm intelligence},
author={Garnier, S. and Gautrais, J. and Theraulaz, G.},
journal={Swarm Intelligence},
volume={1},
pages={3--31},
year={2007}
}
%zitiert von 157 bei Google Scholar
%beim Eintrag nur die notwendigsten Angaben vorhanden; empfehlenswerte Angaben (volume, year und issue/number) fehlen+
%Anmerkung: Artikel allerdings schon älter, sollte bei der endgültigen Version der Arbeit vllt. nicht zitiert werden
@article{wolpert1999introduction,
title={An introduction to collective intelligence},
author={Wolpert, D.H. and Tumer, K.},
journal={Arxiv preprint cs/9908014},
year={1999}
}
%zitiert von 97 bei Google Scholar
%beim Eintrag alle Angaben vorhanden
@article{mondada2005cooperation,
title={The cooperation of swarm-bots: Physical interactions in collective robotics},
author={Mondada, F. and Gambardella, L.M. and Floreano, D. and Nolfi, S. and Deneuborg, J.L. and Dorigo, M.},
journal={Robotics \& Automation Magazine, IEEE},
volume={12},
number={2},
pages={21--28},
year={2005}
}
%zitiert von 125 bei Google Scholar
%beim Eintrag alle Angaben vorhanden
%Anmerkung: Artikel stellt Verbindung zwischen Scharmintelligenz und Physik dar, geht aber eher in Richtung der biologischen Physik; Artikel leider schon von 2000
@article{czirók2000collective,
title={Collective behavior of interacting self-propelled particles},
author={Czir{\'o}k, A. and Vicsek, T.},
journal={Physica A: Statistical Mechanics and its Applications},
volume={281},
number={1},
pages={17--29},
year={2000}
}
%zitiert von 23 bei Google Scholar
%beim Eintrag alle Angaben vorhanden
%Anmerkung: Artikel behandelt eine Erweiterung des Themas "Collective behavior of interacting self-propelled particles"; Artikel ist neuer, von 2008
@article{li2008minimal,
title={Minimal mechanisms for school formation in self-propelled particles},
author={Li, Y.X. and Lukeman, R. and Edelstein-Keshet, L.},
journal={Physica D: Nonlinear Phenomena},
volume={237},
number={5},
pages={699--720},
year={2008}
}
%%Internetartikel
%Internetartikel über Guttenberg und die "Macht der Masse" --> könnte als einführendes Beispiel in der Einleitung verwendet werden; \usepackage{url} im Header laden, damit die URL richtig geladen wird --> stellt dann den Befehl \url{hier Addy eingeben} bei Latex bereit
%alle Angaben beim Eintrag vollständig
@online{GuttenPlagWiki,
author={Matthias Kremp},
title={Im Netz der Plagiate-Jäger},
url={http://www.spiegel.de/netzwelt/web/0,1518,746582,00.html},
year={2011},
urlddate={26.11.2011}
}
% Koriphaen:
% ==============
% Gerardo Beni hat zusammen mit Jin Wang den Begriff "swarm intelligence" im
% Zusammenhang mit Robotik erfunden
@article{beni1993swarm,
title={Swarm intelligence in cellular robotic systems},
author={Beni, G. and Wang, J.},
journal={Robots and Biological Systems: Towards a New Bionics?},
pages={703--712},
year={1993},
publisher={Springer}
}
% James Kennedy
% 15780 citations laut Google Scholar; Suchbegriff: particle swarm optimization
% Das ist wohl DAS paper zu dem Thema!
@inproceedings{Kennedy1995,
author = {Kennedy, J. and Eberhart, R.},
title = {Particle swarm optimization},
booktitle = {Proc. Conf. IEEE Int Neural Networks},
year = {1995},
volume = {4},
pages = {1942--1948},
abstract = {A concept for the <span class='snippet'>optimization</span> of nonlinear
functions using <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
methodology is introduced. The evolution of several paradigms is
outlined, and an implementation of one of the paradigms is discussed.
Benchmark testing of the paradigm is described, and applications,
including nonlinear function <span class='snippet'>optimization</span>
and neural network training, are proposed. The relationships between
<span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> and both artificial life
and genetic algorithms are described},
doi = {10.1109/ICNN.1995.488968},
}
% 4160 citations laut Google Scholar; Suchbegriff: swarm intelligence
@article{kennedy2006swarm,
author = {Kennedy, J.},
title = {Swarm intelligence},
journal = {Handbook of Nature-Inspired and Innovative Computing},
year = {2006},
pages = {187--219},
publisher = {Springer}
}
% 283 citations laut ACM; Suchbegriff: swarm intelligence
@book{Eiben:2003:IEC:954563,
title = {Introduction to Evolutionary Computing},
publisher = {SpringerVerlag},
year = {2003},
author = {Eiben, Agoston E. and Smith, J. E.},
isbn = {3540401849}
}
% 605 citations laut ACM; meistzitiertes Buch zum Suchbegriff: swarm intelligence
@book{Bonabeau:1999:SIN:328320,
title = {Swarm intelligence: from natural to artificial systems},
publisher = {Oxford University Press, Inc.},
year = {1999},
author = {Bonabeau, Eric and Dorigo, Marco and Theraulaz, Guy},
address = {New York, NY, USA},
isbn = {0-19-513159-2}
}
% Folgende Referenzen kommen von IEEE und sind maximal 2 Jahre alt, mit
% dem Bezug von Schwarmintelligenz und Physik/Ingenieurswissenschaften
% Suchbegriffe: swarm intelligence physic
% swarm intelligence engineering
@inproceedings{Jiang2011a,
author = {Jiang, F. and Frater, M. and Ling, S. S. H. },
title = {A distributed smart routing scheme for terrestrial sensor networks
with hybrid Neural Rough Sets},
booktitle = {Proc. IEEE Int Fuzzy Systems (FUZZ) Conf},
year = {2011},
pages = {2238--2244},
abstract = {The limited power consumption, as a major constraint, presents challenges
in improving the network throughput for Wireless Sensor Networks
(WSNs). Due to the limited computational power, the applications
of WSNs in Terrestrial Networks require the capability to pre-process
the observation data so as to remove irrelevant features or factors
from multi-dimensional dataset. This paper proposes a intelligent
distributed energy efficient routing algorithm inspired from natural
learning and adaptation process with the aid of hybrid Neural Rough
Sets theory, which is used to efficiently reduce the dimensionality
of input dataset. The algorithmic implementation and experimental
validation are described in this paper. Details of the algorithm
and its testing procedures are presented in comparison with the other
power-aware protocols, e.g., mini-hop. The validation of the proposed
model is carried out via a wireless sensor network test-bed implemented
in Castalia Simulator. The experimental results show the network
performance measurements such as delay, throughput and packet loss
that have been greatly improved as the outcome of applying this integration
with Neural Rough Sets.},
doi = {10.1109/FUZZY.2011.6007725},
}
@article{Kentzoglanakis2011a,
author = {Kentzoglanakis, K. and Poole, M. },
title = {A Swarm Intelligence Framework for Reconstructing Gene Networks:
Searching for Biologically Plausible Architectures},
journal = IEEE_J_CBB,
year = {2011},
number = {99},
abstract = {In this paper, we investigate the problem of reverse-<span class='snippet'>engineering</span>
the topology of gene regulatory networks from temporal gene expression
data. We adopt a computational <span class='snippet'>intelligence</span>
approach comprising <span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
techniques, namely particle <span class='snippet'>swarm</span> optimization
(PSO) and ant colony optimization (ACO). In addition, the recurrent
neural network (RNN) formalism is employed for modelling the dynamical
behaviour of gene regulatory systems. More specifically, ACO is used
for searching the discrete space of network architectures and PSO
for searching the corresponding continuous space of RNN model parameters.
We propose a novel solution construction process in the context of
ACO for generating biologically plausible candidate architectures.
The objective is to concentrate the search effort into areas of the
structure space that contain architectures which are feasible in
terms of their topological resemblance to real-world networks. The
proposed framework is first applied to an artificial data set with
added noise for reconstructing a subnetwork of the genetic interaction
network of S. cerevisiae (yeast). The framework is also applied to
a real-world data set for reverse-<span class='snippet'>engineering</span>
the SOS response system of the bacterium Escherichia coli. Results
demonstrate the relative advantage of utilizing problem-specific
knowledge regarding biologically plausible structural properties
of gene networks over conducting a problem-agnostic search in the
vast space of network architectures.},
doi = {10.1109/TCBB.2011.87},
}
@inproceedings{Lee2011,
author = {Chang Jun Lee and Prasad, V. and Jong Min Lee},
title = {Robust design of catalysts using stochastic nonlinear optimization},
booktitle = {Proc. Int Advanced Control of Industrial Processes (ADCONIP) Symp},
year = {2011},
pages = {198--203},
abstract = {Computational methods for designing an optimal catalyst have recently
been gaining more popularity in the fields of catalysis and reaction
<span class='snippet'>engineering</span> of energy systems. However,
in general, the problem in these approaches is that uncertainties
present in process models should be handled correctly to achieve
a robust design. To find the optimal design under these uncertainties,
a stochastic <span class='snippet'>optimization</span> method can
be employed. In this work, the optimal properties of a catalyst for
ammonia decomposition to produce hydrogen are investigated, and uncertainties
associated <span class='snippet'>with</span> the reactions and their
parameters are modeled as exogenous uncertain variables which follow
known probability distributions. The goal of this work is to find
the optimal binding energies of the catalyst that maximize conversion
of ammonia in a microreactor. Our stochastic <span class='snippet'>optimization</span>
problem is nonlinear, and involves the expectation operator as well
as integration in the objective function. To tackle this complex
system, the expectation of conversion based on a sample average approximation
(SAA) method is evaluated. However, the exponential increase in the
number of samples to be considered <span class='snippet'>with</span>
the number of uncertain parameters lead to severe computational problems
when using all possible combinations of the uncertain parameters.
To solve this, linearity analysis, together <span class='snippet'>with</span>
partial least squares, is implemented to reduce the number of uncertain
parameters. In the <span class='snippet'>optimization</span> step,
a <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) is employed. The
results indicate that the stochastic optimum shows higher conversion
and different optimal binding energies than the deterministic optimum,
and is a more robust solution.},
}
@inproceedings{Rahmat-Samii2011a,
author = {Rahmat-Samii, Y. },
title = {Let swarms of bees optimize your future communication antennas},
booktitle = {Proc. IEEE Radio and Wireless Symp. (RWS)},
year = {2011},
abstract = {<span class='snippet'>Optimization</span> is the process of upgrading
something to perform better. Engineers constantly look for improving
their designs in multi parametric solution space. Imagine that you
will be able to use nature's evolutionary processes to obtain the
best parameters for your designs. This is the subject of this presentation.
The ever increasing advances in computational power have fueled the
temptation of using global <span class='snippet'>optimization</span>
techniques. The well-known brute force design methodologies are systematically
being replaced by the state-of-the-art Evolutionary <span class='snippet'>Optimization</span>
(EO) techniques. In recent years, EO techniques are finding growing
applications to the design of all kind of systems <span class='snippet'>with</span>
increasing complexity. Among various EO's, nature inspired techniques
such as <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (PSO) have attracted considerable
attention. PSO is a robust stochastic evolutionary computation technique
based on the movement and intelligence of <span class='snippet'>swarms</span>
of bees looking for the most fertile feeding location applying their
cognitive and social knowledge. This presentation will focus on:
(a) an <span class='snippet'>engineering</span> introduction to PSO
by describing in a novel fashion the underlying concepts and recent
advances for those who have used these techniques and for those who
have not had any experiences in these areas, (b) ease of deployment
of PSO on parallel computational platforms, (c) demonstration of
the potential applications of PSO to a variety of communication antenna
designs including advanced cellphone antennas, E-shaped antennas
for multiband and broadband MIMO applications, novel reconfigurable
antennas and array antennas, and (d) assessment of the advantages
and limitations of this technique.},
doi = {10.1109/RWS.2011.5725518},
}
@inproceedings{Lee2011,
author = {Chang Jun Lee and Prasad, V. and Jong Min Lee},
title = {Robust design of catalysts using stochastic nonlinear optimization},
booktitle = {Proc. Int Advanced Control of Industrial Processes (ADCONIP) Symp},
year = {2011},
pages = {198--203},
abstract = {Computational methods for designing an optimal catalyst have recently
been gaining more popularity in the fields of catalysis and reaction
<span class='snippet'>engineering</span> of energy systems. However,
in general, the problem in these approaches is that uncertainties
present in process models should be handled correctly to achieve
a robust design. To find the optimal design under these uncertainties,
a stochastic <span class='snippet'>optimization</span> method can
be employed. In this work, the optimal properties of a catalyst for
ammonia decomposition to produce hydrogen are investigated, and uncertainties
associated <span class='snippet'>with</span> the reactions and their
parameters are modeled as exogenous uncertain variables which follow
known probability distributions. The goal of this work is to find
the optimal binding energies of the catalyst that maximize conversion
of ammonia in a microreactor. Our stochastic <span class='snippet'>optimization</span>
problem is nonlinear, and involves the expectation operator as well
as integration in the objective function. To tackle this complex
system, the expectation of conversion based on a sample average approximation
(SAA) method is evaluated. However, the exponential increase in the
number of samples to be considered <span class='snippet'>with</span>
the number of uncertain parameters lead to severe computational problems
when using all possible combinations of the uncertain parameters.
To solve this, linearity analysis, together <span class='snippet'>with</span>
partial least squares, is implemented to reduce the number of uncertain
parameters. In the <span class='snippet'>optimization</span> step,
a <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) is employed. The
results indicate that the stochastic optimum shows higher conversion
and different optimal binding energies than the deterministic optimum,
and is a more robust solution.},
}
@inproceedings{Rahmat-Samii2011a,
author = {Rahmat-Samii, Y. },
title = {Let swarms of bees optimize your future communication antennas},
booktitle = {Proc. IEEE Radio and Wireless Symp. (RWS)},
year = {2011},
abstract = {<span class='snippet'>Optimization</span> is the process of upgrading
something to perform better. Engineers constantly look for improving
their designs in multi parametric solution space. Imagine that you
will be able to use nature's evolutionary processes to obtain the
best parameters for your designs. This is the subject of this presentation.
The ever increasing advances in computational power have fueled the
temptation of using global <span class='snippet'>optimization</span>
techniques. The well-known brute force design methodologies are systematically
being replaced by the state-of-the-art Evolutionary <span class='snippet'>Optimization</span>
(EO) techniques. In recent years, EO techniques are finding growing
applications to the design of all kind of systems <span class='snippet'>with</span>
increasing complexity. Among various EO's, nature inspired techniques
such as <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (PSO) have attracted considerable
attention. PSO is a robust stochastic evolutionary computation technique
based on the movement and intelligence of <span class='snippet'>swarms</span>
of bees looking for the most fertile feeding location applying their
cognitive and social knowledge. This presentation will focus on:
(a) an <span class='snippet'>engineering</span> introduction to PSO
by describing in a novel fashion the underlying concepts and recent
advances for those who have used these techniques and for those who
have not had any experiences in these areas, (b) ease of deployment
of PSO on parallel computational platforms, (c) demonstration of
the potential applications of PSO to a variety of communication antenna
designs including advanced cellphone antennas, E-shaped antennas
for multiband and broadband MIMO applications, novel reconfigurable
antennas and array antennas, and (d) assessment of the advantages
and limitations of this technique.},
doi = {10.1109/RWS.2011.5725518},
}
@article{Kentzoglanakis2011a,
author = {Kentzoglanakis, K. and Poole, M. },
title = {A Swarm Intelligence Framework for Reconstructing Gene Networks:
Searching for Biologically Plausible Architectures},
journal = IEEE_J_CBB,
year = {2011},
number = {99},
abstract = {In this paper, we investigate the problem of reverse-<span class='snippet'>engineering</span>
the topology of gene regulatory networks from temporal gene expression
data. We adopt a computational <span class='snippet'>intelligence</span>
approach comprising <span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
techniques, namely particle <span class='snippet'>swarm</span> optimization
(PSO) and ant colony optimization (ACO). In addition, the recurrent
neural network (RNN) formalism is employed for modelling the dynamical
behaviour of gene regulatory systems. More specifically, ACO is used
for searching the discrete space of network architectures and PSO
for searching the corresponding continuous space of RNN model parameters.
We propose a novel solution construction process in the context of
ACO for generating biologically plausible candidate architectures.
The objective is to concentrate the search effort into areas of the
structure space that contain architectures which are feasible in
terms of their topological resemblance to real-world networks. The
proposed framework is first applied to an artificial data set with
added noise for reconstructing a subnetwork of the genetic interaction
network of S. cerevisiae (yeast). The framework is also applied to
a real-world data set for reverse-<span class='snippet'>engineering</span>
the SOS response system of the bacterium Escherichia coli. Results
demonstrate the relative advantage of utilizing problem-specific
knowledge regarding biologically plausible structural properties
of gene networks over conducting a problem-agnostic search in the
vast space of network architectures.},
doi = {10.1109/TCBB.2011.87},
}
@inproceedings{Jiang2011a,
author = {Jiang, F. and Frater, M. and Ling, S. S. H. },
title = {A distributed smart routing scheme for terrestrial sensor networks
with hybrid Neural Rough Sets},
booktitle = {Proc. IEEE Int Fuzzy Systems (FUZZ) Conf},
year = {2011},
pages = {2238--2244},
abstract = {The limited power consumption, as a major constraint, presents challenges
in improving the network throughput for Wireless Sensor Networks
(WSNs). Due to the limited computational power, the applications
of WSNs in Terrestrial Networks require the capability to pre-process
the observation data so as to remove irrelevant features or factors
from multi-dimensional dataset. This paper proposes a intelligent
distributed energy efficient routing algorithm inspired from natural
learning and adaptation process with the aid of hybrid Neural Rough
Sets theory, which is used to efficiently reduce the dimensionality
of input dataset. The algorithmic implementation and experimental
validation are described in this paper. Details of the algorithm
and its testing procedures are presented in comparison with the other
power-aware protocols, e.g., mini-hop. The validation of the proposed
model is carried out via a wireless sensor network test-bed implemented
in Castalia Simulator. The experimental results show the network
performance measurements such as delay, throughput and packet loss
that have been greatly improved as the outcome of applying this integration
with Neural Rough Sets.},
doi = {10.1109/FUZZY.2011.6007725},
}
@article{Jin2010,
author = {Nanbo Jin and Rahmat-Samii, Y. },
title = {Hybrid Real-Binary Particle Swarm Optimization (HPSO) in Engineering
Electromagnetics},
journal = IEEE_J_AP,
year = {2010},
volume = {58},
pages = {3786--3794},
number = {12},
abstract = {The applications of a hybrid real-binary <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
(HPSO) algorithm in <span class='snippet'>engineering</span> electromagnetics
are described. In HPSO, each candidate design is designated by a
hybridized vector consisting of both real and binary variables. These
variables are evolved in the <span class='snippet'>optimization</span>
by following the velocity/position updating formulas of real-number
PSO (RPSO) and binary PSO (BPSO), respectively. Both single- and
multi-objective implementations of the algorithm are validated by
functional testbeds. Simulation and measurement results of three
examples, i.e, the design of a non-uniform antenna array, a multilayered
planar radar absorbing material (RAM) and a dual-band handset antenna
are presented, in order to illustrate the effectiveness of the algorithm
in representative topology exploration and material selection problems.},
doi = {10.1109/TAP.2010.2078477},
}
@inproceedings{Basak2010,
author = {Basak, A. and Pal, S. and Das, S. and Abraham, A. },
title = {Circular antenna array synthesis with a Differential Invasive Weed
Optimization algorithm},
booktitle = {Proc. 10th Int Hybrid Intelligent Systems (HIS) Conf},
year = {2010},
pages = {153--158},
abstract = {In this article we describe an <span class='snippet'>optimization</span>-based
design method for non-uniform, planar, and circular antenna arrays
<span class='snippet'>with</span> the objective of achieving minimum
side lobe levels for a specific first null beamwidth and also a minimum
size of the circumference. Central to our design is a hybridization
of two prominent metaheuristics of current interest namely the Invasive
Weed <span class='snippet'>Optimization</span> (IWO) and the Differential
Evolution (DE). IWO is a derivative-free real parameter <span class='snippet'>optimization</span>
technique that mimics the ecological behavior of colonizing weeds.
Owing to its superior performance in comparison <span class='snippet'>with</span>
many other existing metaheuristics, recently IWO is being used in
several <span class='snippet'>engineering</span> design problems
from diverse domains. For the present application, we have modified
classical IWO by incorporating the difference vector based mutation
schemes from the realm of DE. Three difficult instances of the circular
array design problem have been presented to illustrate the effectiveness
of the hybrid Differential IWO (DIWO) algorithm. The design results
obtained <span class='snippet'>with</span> modified IWO have been
shown to comfortably outperform the results obtained <span class='snippet'>with</span>
other state-of-the-art metaheuristics like <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO), and Differential Evolution (DE) in a statistically significant
fashion.},
doi = {10.1109/HIS.2010.5600021},
}
@inproceedings{Yan2010,
author = {Qiao Yan and ChangBin Wu and Songzhao Lv and MingLiang Bi},
title = {Displacement Back-Analysis of rock-fill dam based on particle swarm
optimization and genetic neural network algorithm},
booktitle = {Proc. Int Computer Application and System Modeling (ICCASM) Conf},
year = {2010},
volume = {5},
abstract = {In view of the complexity of Back Analysis of rock-fill material parameter,
this paper uses genetic algorithm <span class='snippet'>optimization</span>
BP neural network weights and threshold, simulated finite element
calculation of rockfill dam by genetic neural network, combined <span
class='snippet'>with</span> the theory of <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
algorithm, and has realized inverse analysis of <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
and genetic neural network. The results showed that: the simulation
result coincides well <span class='snippet'>with</span> the experimental
result, and inversion results could meet the computing requirements.
This method can be widely used to solve various complicated <span
class='snippet'>engineering</span> problems that target functions
can't be expressed by apparent functions of decisive variables.},
doi = {10.1109/ICCASM.2010.5620126},
}
@article{Goudos2010,
author = {Goudos, S. K. and Sahalos, J. N. },
title = {Pareto Optimal Microwave Filter Design Using Multiobjective Differential
Evolution},
journal = IEEE_J_AP,
year = {2010},
volume = {58},
pages = {132--144},
number = {1},
abstract = {Microwave filters play an important role in modern wireless communications.
A novel method for the design of multilayer dielectric and open loop
ring resonator (OLRR) filters under constraints is presented. The
proposed design method is based on generalized differential evolution
(GDE3), which is a multiobjective extension of differential evolution
(DE). GDE3 algorithm can be applied for global <span class='snippet'>optimization</span>
to any <span class='snippet'>engineering</span> problem <span class='snippet'>with</span>
an arbitrary number of objective and constraint functions. GDE3 is
compared against other evolutionary multiobjective algorithms like
nondominated sorting genetic algorithm-II (NSGA-II), multiobjective
<span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (MOPSO) and multiobjective
<span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> <span class='snippet'>with</span>
fitness sharing (MOPSO-fs) for a number of microwave filter design
cases. In the multilayer dielectric filter design case a predefined
database of low loss dielectric materials is used. The results indicate
the advantages of this approach and the applicability of this design
method.},
doi = {10.1109/TAP.2009.2032100},
}
@inproceedings{Qiao2010,
author = {Zhihe Qiao and Hongyan Zhang},
title = {Research on Estimation Methods of Internal Rate of Return in Hydraulic
Engineering Project},
booktitle = {Proc. 2nd Int Information Engineering and Computer Science (ICIECS)
Conf},
year = {2010},
pages = {1--4},
abstract = {The internal rate of return method is currently an important analysis
tool for economic feasibility evaluation of <span class='snippet'>engineering</span>
project. Against the disadvantages of general methods to calculate
internal rate of return, such as big calculation amount, slow speed
and low result precision, the calculation of internal rate of return
in <span class='snippet'>engineering</span> project was equivalently
derived into an <span class='snippet'>optimization</span> problem.
Then <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> algorithm and ant colony
<span class='snippet'>optimization</span> algorithm were applied
to pursuit the optimal solution. A hydraulic <span class='snippet'>engineering</span>
project was taking as an example and the analysis results indicated
that the two methods used in the paper were simple, effective and
precise, which can be extensively used in <span class='snippet'>engineering</span>
projects.},
doi = {10.1109/ICIECS.2010.5678145},
}
@inproceedings{Djuric2010e,
author = {Djuric, Petar M. },
title = {From nature to methods and back to nature},
booktitle = {Proc. Int Security and Cryptography (SECRYPT) Conf},
year = {2010},
abstract = {A fundamental challenge in today's arena of complex systems is the
design and development of accurate and robust signal processing methods.
These methods should be capable to adapt quickly to unexpected changes
in the data and operate under minimal model assumptions. Systems
in Nature also do signal processing and often do it optimally. Therefore,
it makes much sense to understand what Nature does and try to mimic
it and do even better. In return, the results of better signal processing
methods may lead to new advancements in science and technology and
in understanding Nature. In this presentation methods for signal
processing that borrow concepts and principles found in Nature are
addressed including ant <span class='snippet'>optimization</span>,
<span class='snippet'>swarm</span> intelligence and genetic algorithms.
However, the emphasis of the presentation is on Monte Carlo-based
methods, and in particular, methods related to <span class='snippet'>particle</span>
filtering, cost-reference <span class='snippet'>particle</span> filtering,
and population Monte Carlo. In the past decade and a half, Monte
Carlo-based methods have gained considerable popularity in dealing
<span class='snippet'>with</span> nonlinear and/or non-Gaussian systems.
The three groups of methods share the feature that they explore spaces
of unknowns using <span class='snippet'>particles</span> and weights
(costs) assigned to the <span class='snippet'>particles</span>. In
most versions of these methods, <span class='snippet'>particles</span>
move independently and in accordance <span class='snippet'>with</span>
the dynamics of the assumed model of the states. Interactions among
<span class='snippet'>particles</span> only occur through the process
of resampling rather than through local interactions as is common
in physical and biological systems. Such interactions can improve
the performance of the methods and can allow for coping <span class='snippet'>with</span>
more challenging problems <span class='snippet'>with</span> better
efficiency and accuracy. We show how we apply these methods to problems
in <span class='snippet'>engineering</span>, economics, and biology.},
}
@inproceedings{Caputo2010,
author = {Caputo, D. and Grimaccia, F. and Mussetta, M. and Zich, R. E.
},
title = {Photovoltaic plants predictive model by means of ANN trained by a
hybrid evolutionary algorithm},
booktitle = {Proc. Int Neural Networks (IJCNN) Joint Conf},
year = {2010},
pages = {1--6},
abstract = {This paper introduces a hybrid evolutionary <span class='snippet'>optimization</span>
algorithm as a tool for training an Artificial Neural Network used
for production forecasting of solar energy PV plants. This hybrid
technique is developed in order to exploit in the most effective
way the uniqueness and peculiarities of two classical <span class='snippet'>optimization</span>
approaches, <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (PSO) and Genetic Algorithms
(GA). This procedure essentially represent a bio-inspired heuristic
search technique, which can be used to solve combinatorial <span
class='snippet'>optimization</span> problems, modeled on the concepts
of natural selection and evolution (GA), but also based on cultural
and social behaviours derived from the analysis of the <span class='snippet'>swarm</span>
intelligence and interaction among <span class='snippet'>particles</span>
(PSO). Some simulation results are reported to highlight advantages
and drawbacks of the proposed technique in order to suitably apply
this algorithm to neural network applications in <span class='snippet'>engineering</span>
problems.},
doi = {10.1109/IJCNN.2010.5596782},
}
@inproceedings{Jiang2010,
author = {Annan Jiang and Junxiang Wang and Jiao Zhang},
title = {Identifying the tunnel surrounding rock parameters based on particle
swarm optimization arithmetic},
booktitle = {Proc. Int Intelligent Control and Information Processing (ICICIP)
Conf},
year = {2010},
pages = {197--201},
abstract = {Because numerical model complexity of tunnel, the paper induces a
<span class='snippet'>swarm</span> intelligence <span class='snippet'>optimization</span>
algorithm - improved <span class='snippet'>particle</span> <span
class='snippet'>swarm</span> <span class='snippet'>optimization</span>,
constructs the surrounding rock parameters identification method.
Based on orthogonal design and uniformity design, the numerical tests
of underground <span class='snippet'>engineering</span> are carried
out, so as to produce data set to gain the regress model relating
rock mechanical parameters and monitoring displacements. Begin from
the random values of parameters, take error between calculation values
and monitoring data as fitness value, iterating according to the
PSO rule, then the parameters adaptive identification realized. True
tunnel <span class='snippet'>engineering</span> calculation states
that the mean has good global <span class='snippet'>optimization</span>
ability, the recognition accuracy is satisfied.},
doi = {10.1109/ICICIP.2010.5565303},
}
@inproceedings{Cai2010a,
author = {Jianghui Cai and Wenjun Meng},
title = {A New Parameters Optimization Method for Screw Conveyor Based on
PSO},
booktitle = {Proc. 3rd Int Emerging Trends in Engineering and Technology (ICETET)
Conf},
year = {2010},
pages = {263--266},
abstract = {<span class='snippet'>Particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) is a population based
stochastic <span class='snippet'>optimization</span> technique. As
a result, PSO algorithm is widely used in mechanical <span class='snippet'>engineering</span>
design field. Screw conveyors are used extensively in agriculture
and processing industries for elevating and/or transporting bulk
materials over short to medium distances. They are very effective
for conveying dry particulate solids, giving good control over the
throughput. Despite their apparent simplicity, the transportation
action is very complex and designers have tended to rely heavily
on empirical performance data. Intelligent operation parameters <span
class='snippet'>optimization</span> for screw conveyor based on PSO
is studied in this paper. This thesis takes a heavy driving drum
of screw conveyor as an example, firstly, the <span class='snippet'>optimization</span>
function is built up, then the operation parameter of screw conveyor
is precision optimized <span class='snippet'>with</span> PSO algorithm,
which offer a foundation to design more reasonable structure for
driving drum in order to meet the application demands.},
doi = {10.1109/ICETET.2010.141},
}
@article{Chen2010,
author = {An-Pin Chen and Yu-Chia Hsu},
title = {Dynamic Physical Behavior Analysis for Financial Trading Decision
Support [Application Notes]},
journal = IEEE_M_CIM,
year = {2010},
volume = {5},
pages = {19--23},
number = {4},
abstract = {This article aims to discuss the application of computational <span
class='snippet'>intelligence</span> (CI) techniques in combination
"with classical concepts in <span class='snippet'>physics</span>
in devising investment strategies. In the analysis of investment
strategies, many CI techniques are employed to predict market trends,
such as the neural network (NN), the support vector machine (SVM),
and particle <span class='snippet'>swarm</span> optimization (PSO)
techniques. Other techniques such as evolutionary computing (EC)
and genetic algorithm (GA) are utilized to identify the knowledge
rules of trading. However, changes in market behavior are dynamic
and time variant. Thus, using a single CI technique can occasionally
be better than traditional statistic models, but the trading models
may pose risks from the changing market. Recently, the hybrid model
and the data mining concept, "which combine multiple CI techniques
into multiple stages, have emerged to improve the trading model's
stability and profitability. For example, fuzzy logic is employed
to differentiate the parameters in the first stage, and then similarity
search is used for data clustering in the second stage.},
doi = {10.1109/MCI.2010.938366},
}
@article{Liu2010,
author = {Lili Liu and Shengxiang Yang and Dingwei Wang},
title = {Particle Swarm Optimization With Composite Particles in Dynamic Environments},
journal = IEEE_J_SMCB,
year = {2010},
volume = {40},
pages = {1634--1648},
number = {6},
abstract = {In recent years, there has been a growing interest in the study of
particle <span class='snippet'>swarm</span> optimization (PSO) in
dynamic environments. This paper presents a new PSO model, called
PSO with composite particles (PSO-CP), to address dynamic optimization
problems. PSO-CP partitions the <span class='snippet'>swarm</span>
into a set of composite particles based on their similarity using
a &#x201C;worst first&#x201D; principle. Inspired by the composite
particle phenomenon in <span class='snippet'>physics</span>, the
elementary members in each composite particle interact via a velocity-anisotropic
reflection scheme to integrate valuable information for effectively
and rapidly finding the promising optima in the search space. Each
composite particle maintains the diversity by a scattering operator.
In addition, an integral movement strategy is introduced to promote
the <span class='snippet'>swarm</span> diversity. Experiments on
a typical dynamic test benchmark problem provide a guideline for
setting the involved parameters and show that PSO-CP is efficient
in comparison with several state-of-the-art PSO algorithms for dynamic
optimization problems.},
doi = {10.1109/TSMCB.2010.2043527},
}
@inproceedings{Wei2010,
author = {Kou Wei and Sun Feng-rui and Yang Li and Chen Lin-gen},
title = {Application of BCC Algorithm and RBFNN in Identification of Defect
Parameters},
booktitle = {Proc. Int Intelligence Information Processing and Trusted Computing
(IPTC) Symp},
year = {2010},
pages = {638--642},
abstract = {The identification of defect parameters in thermal non-destructive
test and evaluation (NDT/E) was considered as a kind of inverse heat
transfer problem (IHTP). However, it can be farther considered as
a shape optimization problem then a structure design optimization
problem, and the design results should meet the surface temperature
profile of the apparatus with defects. A bacterial colony chemotaxis
(BCC) optimization algorithm and a radial basis function neural network
(RBFNN) are applied to the thermal NDT/E for the identification of
defects parameters. The RBFNN is a precise and convenient surrogate
model for the time costly finite element computation, which obtains
the surface temperature with different defect parameters. The BCC
optimization algorithm is derivatively-free, and the convergence
speed is fast. This method is applied to a simple verification case
and the result is acceptable. The algorithm is also compared with
the particle <span class='snippet'>swarm</span> optimization (PSO)
algorithm, and the BCC algorithm can access the optimum with faster
speed.},
doi = {10.1109/IPTC.2010.15},
}
@inproceedings{Zhu2010a,
author = {Yan-fei Zhu and Xiong-min Tang},
title = {Overview of swarm intelligence},
booktitle = {Proc. Int Computer Application and System Modeling (ICCASM) Conf},
year = {2010},
volume = {9},
abstract = {This paper gives a broad overview of <span class='snippet'>swarm</span>
<span class='snippet'>intelligence</span> in three parts: biological
basis, artificial literature and <span class='snippet'>swarm</span>
<span class='snippet'>engineering</span>. In biological basis part,
the paper gives some operational principles from biological systems
by naturalists and biologists. In artificial literature part, two
fundamental approaches are provided to analyze <span class='snippet'>swarm</span>
topology. The prevalent <span class='snippet'>swarm</span> models
and techniques such as Reynolds's rules, discrete and continuum theory
of flocking, coordination stability of the <span class='snippet'>swarm</span>
motion, etc, are also summarized in this part. In <span class='snippet'>swarm</span>
<span class='snippet'>engineering</span> part, the paper discusses
Kazadi's &#x201C;two-step&#x201D; process. Many <span class='snippet'>engineering</span>
applications come from Kazadi's researches. Also, the main application
of <span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
on robot systems and other applications are introduced in this part.
We say this paper provides concepts for a better understanding of
<span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
both in principles and in applications.},
doi = {10.1109/ICCASM.2010.5623005},
}
@inproceedings{Modarres2009,
author = {Modarres, H. and Alfi, A. },
title = {A particle Swarm Optimization approach for parameter identification
of Lorenz chaotic system},
booktitle = {Proc. 35th Annual Conf. of IEEE Industrial Electronics IECON '09},
year = {2009},
pages = {3303--3308},
abstract = {An important problem in <span class='snippet'>engineering</span> is
the identification of nonlinear systems, among them chaotic systems
have received particular attention due to their complex and unpredictable
behaviors. In this paper, a <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO) technique is applied for online parameter identification of
Lorenz chaotic system. The difficulties of online implementation
mainly come from the unavoidable computational time to find a solution.
Due to this, first an Improved <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(IPSO) is proposed to increase the convergence speed and accuracy
of the Standard <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (SPSO) to save tremendous
computation time. Second, IPSO is also improved to detect and determine
the variation of parameters. Finally, a numerical example is given
to verify the effectiveness of the proposed method compared to Genetic
Algorithm (GA) and SPSO.},
doi = {10.1109/IECON.2009.5415058},
}
@inproceedings{Wang2009,
author = {Lei Wang and Yuwen Zhou and Weiwei Zhao},
title = {Comparative Study on Bionic Optimization Algorithms for Sewer Optimal
Design},
booktitle = {Proc. Fifth Int. Conf. Natural Computation ICNC '09},
year = {2009},
volume = {3},
pages = {24--29},
abstract = {Sewer network as a necessary urban infrastructure plays an important
role in people's daily life. Conventional <span class='snippet'>optimization</span>
techniques have significant limitations on solving the problems of
sewer optimal design. Because as a high-dimensional discrete complex
<span class='snippet'>optimization</span> problem, sewer optimal
design is characterized by its discrete objective function and, as
an integer discrete variable, its decision variable amount keeps
the same pace <span class='snippet'>with</span> <span class='snippet'>engineering</span>
scales. Over the last decade, various kinds of modern bionic <span
class='snippet'>optimization</span> algorithms <span class='snippet'>with</span>
their special advantages have been created and applied into sewer
optimal design successfully. Based on previous studies, this paper
analyses and compares the solution performances of genetic algorithms
(GA), <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) and ant colony algorithms
(ACA) from the three aspects respectively, they are convergence,
speed and complexity of algorithm. The research result shows that
compared <span class='snippet'>with</span> the other two algorithms,
the ACA manifests its superiority for better convergence, satisfactory
speed and relatively small algorithm complexity, which are very suitable
for solving the problems of sewer optimal design.},
doi = {10.1109/ICNC.2009.89},
}
@inproceedings{Korani2009,
author = {Korani, W. M. and Dorrah, H. T. and Emara, H. M. },
title = {Bacterial foraging oriented by Particle Swarm Optimization strategy
for PID tuning},
booktitle = {Proc. IEEE Int Computational Intelligence in Robotics and Automation
(CIRA) Symp},
year = {2009},
pages = {445--450},
abstract = {Proportional integral derivative (PID) controller tuning is an area
of interest for researchers in many disciplines of science and <span
class='snippet'>engineering</span>. This paper presents a new algorithm
for PID controller tuning based on a combination of the foraging
behavior of E coli bacteria foraging and <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO). The E coli algorithm depends on random search directions which
may lead to delay in reaching the global solution. The PSO algorithm
may lead to possible entrapment in local minimum solutions. This
paper proposed a new algorithm Bacteria Foraging oriented by PSO
(BF-PSO). The new algorithm is proposed to combines both algorithms'
advantages in order to get better <span class='snippet'>optimization</span>
values. The proposed algorithm is applied to the problem of PID controller
tuning and is compared <span class='snippet'>with</span> conveniently
Bacterial Foraging algorithm and <span class='snippet'>Particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>.},
doi = {10.1109/CIRA.2009.5423165},
}
@inproceedings{Wu2009,
author = {Liu Wu and Liu Yi and Gu Xue-qin and Wang Deng-hai},
title = {Optimum Distribution of Aseismic Engineering Investment for Gas Pipeline
Network System Based on Improved Particle Swarm Algorithm},
booktitle = {Proc. Second Int. Conf. Intelligent Computation Technology and Automation
ICICTA '09},
year = {2009},
volume = {3},
pages = {249--252},
abstract = {Under the limit of <span class='snippet'>engineering</span> investment,
the aseismic optimum design of pipeline system is used to minimize
the sum of aseismic cost. Since optimal problem is an nonlinear hybrid
model which involves dual "curse of dimensionality" during the mathematical
programming and system reliability calculation, traditional optimal
techniques can hardly solve the problems of distribution of aseismic
<span class='snippet'>engineering</span> investment for lifeline
network system. Evolutionary-based <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> algorithm has been investigated
and the improved design of inertia weight has been fulfilled in this
paper. A new hybrid <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
algorithm makes use of the ergodicity of chaos to improve the capability
of precise search and keep the balance between the global search
and the local search. Combining <span class='snippet'>with</span>
Boolean cubical matrix disjoint calculation method used as aseismic
reliability analysis tool of network system, the improved algorithm
is applied to the <span class='snippet'>optimization</span> solution
of investment distribution for gas pipeline network .The analysis
of an example in a certain pipeline network system demonstrates that
the established model and algorithm can achieve better convergent
performance and speed.},
doi = {10.1109/ICICTA.2009.527},
}
@inproceedings{Hu2009,
author = {Min Hu and Fang-Fang Wu},
title = {Improved multi-classes SVM and application in ring-key position selection
in tunnels},
booktitle = {Proc. Int Machine Learning and Cybernetics Conf},
year = {2009},
volume = {2},
pages = {1094--1099},
abstract = {The universal trapezoidal tapered ring has been widespread applied
in the tunnel <span class='snippet'>engineering</span>. However,
it is difficult to select rational ring-key position location. As
it can be defined as a restrictive multi-classes problem, traditional
method cannot be used directly. This paper proposed a novel method,
CBPSO-RMCSVM, which combines chaos binary <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
(CBPSO) <span class='snippet'>with</span> restrictive multi-classes
support vector machine (RMCSVM), so as to satisfied actual <span
class='snippet'>engineering</span> demand and improve the accuracy
and efficiency. <span class='snippet'>With</span> the guidance of
CBPSO-RMCSVM, a new decision support system of universal trapezoidal
tapered ring-key position selection in shield tunneling was designed
and developed. The system was applied to the Shanghai Yangtze River
tunnel project successfully, and the <span class='snippet'>engineering</span>
result showed the improved method had good performance.},
doi = {10.1109/ICMLC.2009.5212404},
}
@inproceedings{Abdul-Malek2009,
author = {Abdul-Malek, M. A. and Abido, M. A. },
title = {STATCOM based controller design using Particle Swarm Optimization
for power system stability enhancement},
booktitle = {Proc. IEEE Int. Symp. Industrial Electronics ISIE 2009},
year = {2009},
pages = {1218--1223},
abstract = {In recent years, there has been an increasing interest on applying
advanced and intelligent control designs in power <span class='snippet'>engineering</span>
area. In this paper, damping controllers for a Single Machine Infinite
Bus (SMIB) System <span class='snippet'>with</span> STATic synchronous
COMpensator (STATCOM) are designed using <span class='snippet'>swarm</span>
intelligence. The controller design problem is formulated as an <span
class='snippet'>optimization</span> problem where <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO) is proposed to find optimal controller parameters. Nonlinear
time domain simulation based multi-objective function is formulated
to minimize time-weighted errors in order to damp system oscillations
of the nonlinear system model. The effectiveness of the designed
controllers is examined under severe system disturbances. The results
show that the system performance <span class='snippet'>with</span>
the proposed controllers is quite satisfactory and confirm the quality
of the damping characteristics of the designed PSO based controllers.},
doi = {10.1109/ISIE.2009.5214495},
}
@inproceedings{Zeng2009,
author = {Junwei Zeng and Li Wang and Tingliang Wang and Wenzhuo Fan and Hui
Gao},
title = {Particle swarm optimization-based machine arrangement for filling
construction of rock-fill dams},
booktitle = {Proc. IEEE Int. Conf. Industrial Engineering and Engineering Management
IEEM 2009},
year = {2009},
pages = {1772--1775},
abstract = {The factors that can influence the equipment <span class='snippet'>optimization</span>
in filling construction of rock-fill dam are explained by analyzing
the filling system of rock-fill dams in this paper, and an <span
class='snippet'>optimization</span> model for equipment organization
of fill construction of rock-fill dam is established, in which the
minimum of unbalanced degree of filling intensity is taken as the
objective function <span class='snippet'>with</span> constraint.
The quantum discrete <span class='snippet'>particle</span> <span
class='snippet'>swarm</span> <span class='snippet'>optimization</span>
is used in the model solution. Based on the <span class='snippet'>optimization</span>
model and algorithm, the hydraulic <span class='snippet'>engineering</span>
construction management decision support system is developed by using
an object-oriented methodology. The system is applied to a practical
project successfully to obtain the optimum solution of equipment
organization.},
doi = {10.1109/IEEM.2009.5373163},
}
% IEEE: swarm intelligence power plant, Paper hat was mit Kraftwerken zu tun
@inproceedings{Affijulla2011a,
author = {Affijulla, S. and Chauhan, S. },
title = {A new intelligence solution for power system economic load dispatch},
booktitle = {Proc. 10th Int Environment and Electrical Engineering (EEEIC) Conf},
year = {2011},
pages = {1--5},
abstract = {Economic Load Dispatch is one of the most important tasks to be performed
in the operation and planning of a <span class='snippet'>power</span>
system that decides the generation schedule of generating units with
an objective of minimizing the total fuel cost. Normally, the fuel
cost of generators can be treated as a quadratic function of real
<span class='snippet'>power</span> generation. In fact, valve point
loading effect in thermal <span class='snippet'>power</span> <span
class='snippet'>plants</span> introduces discontinuity. The classical
optimization methods require continuous differentiable objective
functions; therefore they at times provide global minima. The evolutionary
computation methods can handle non-differential and non-convex objective
functions and provide global or near global optimum solutions. Evolutionary
techniques such as Genetic Algorithm (GA), Differential Evolution
(DE), and Particle <span class='snippet'>Swarm</span> Optimization
(PSO) saw wide applications in economic load dispatch. Similar to
evolutionary computation, physical behaviour <span class='snippet'>intelligence</span>
called gravitational search <span class='snippet'>intelligence</span>
is recently developed and has not been applied in many fields. Use
of gravitational search <span class='snippet'>intelligence</span>
not only avoids coding and monotonous decoding as prevalent in transformations
of GA but also results in less burden on parameter settings, population
size, number of iterations and no memory requirement of solution
as in PSO. In this paper, Gravitational Search algorithm (GSA) is
applied to economic load dispatch problem with valve point loading
and Kron's loss. Its performance is compared for accuracy and speed
with contemporaries heuristic search techniques like PSO, DE, and
GA and traditional method sequential quadratic programming (SQP)
on 3, 6, 13 and 40-unit test systems. The simulation results reveal
that GSA has a great potential in handling complex optimization problems
and capable to discover quality solution quickly even for large scale
systems.},
doi = {10.1109/EEEIC.2011.5874614},
}
% IEEE: swarm intelligence power plant, Paper hat was mit Kraftwerken zu tun
@inproceedings{Qiao2011,
author = {Wei Qiao and Jiaqi Liang and Venayagamoorthy, G. K. and Harley,
R. },
title = {Computational intelligence for control of wind turbine generators},
booktitle = {Proc. IEEE Power and Energy Society General Meeting},
year = {2011},
pages = {1--6},
abstract = {This paper summarizes past and ongoing research in the area of the
application of computational <span class='snippet'>intelligence</span>
(CI) for control of wind turbine generators (WTGs). Several intelligent
design approaches and control strategies, including optimal design
of WTG controllers using particle <span class='snippet'>swarm</span>
optimization (PSO) and mean-variance optimization (MVO) algorithms
and adaptive critic design-based coordinated optimal adaptive control
for wind <span class='snippet'>plants</span> and shunt FACTS devices,
are presented for dynamic performance and fault ride-through enhancement
of WTGs and the associated <span class='snippet'>power</span> grid.
The effectiveness of these intelligent design approaches and control
strategies are demonstrated by nonreal- and real-time simulations
in PSCAD/EMTDC and RSCAD/RTDS, respectively.},
doi = {10.1109/PES.2011.6039778},
}
% IEEE: swarm intelligence power plant, Paper hat was mit Kraftwerken zu tun
@article{Faria2011,
author = {Faria, P. and Vale, Z. and Soares, J. and Ferreira, J. },
title = {Demand Response Management in Power Systems Using a Particle Swarm
Optimization Approach},
journal = IEEE_M_IS,
year = {2011},
number = {99},
abstract = {Demand response (DR) is not a new concept but it is gaining a growing
focus of attention in nowadays electric <span class='snippet'>power</span>
systems operation and planning, with several advantages for the reliable
<span class='snippet'>power</span> system functioning and for electricity
prices. In this paper, price-based DR is applied to electricity consumers
through the management of electricity prices. This management is
based on demand elasticity and consumers are expected to react enabling
to accomplish the required load reduction. The methodology is implemented
in a developed DR simulator &amp;#x2013; DemSi - that uses PSCAD??
for technical validation of solutions and Particle <span class='snippet'>Swarm</span>
Optimization (PSO) for solution optimization. The performance of
PSO is evaluated in terms of running time and obtained solutions
in comparison with the Non-Linear Programming (NLP) solutions obtained
in GAMS&amp;#x2122;. Case studies involving 32 and 320 consumers
are used to illustrate the proposed methodology and to discuss its
performance.},
doi = {10.1109/MIS.2011.35},
}
% IEEE: swarm intelligence power plant, Paper hat was mit Kraftwerken zu tun
@inproceedings{Mushtaha2011,
author = {Mushtaha, M. and Krost, G. },
title = {Sizing a self-sustaining wind-Diesel power supply by Particle Swarm
Optimization},
booktitle = {Proc. IEEE Symp. Computational Intelligence Applications In Smart
Grid (CIASG)},
year = {2011},
pages = {1--7},
abstract = {Hybrid wind-Diesel systems can be an attractive solution for the supply
of remotely located consumers or the provision of enhanced energy
independence in the Near-East region, especially in Gaza strip. Such
stand-alone system contains a wind turbine, batteries as short term
storage as well as hydrogen storage tanks as long term storage devices.
The proposed system leads to a remarkable reduction in the fuel consumption,
in comparison with Diesel only systems. For proper sizing of the
particular system components under minimization of the Life Cycle
Cost (LCC), the metaheuristic computational method of Particle <span
class='snippet'>Swarm</span> Optimization (PSO) was successfully
applied; this is exemplarily shown by the electricity supply of a
hospital in Gaza strip where the energy requirements are fully met
under regard of specific constraints and restrictions.},
doi = {10.1109/CIASG.2011.5953192},
}
@inproceedings{Basak2010,
author = {Basak, A. and Pal, S. and Das, S. and Abraham, A. },
title = {Circular antenna array synthesis with a Differential Invasive Weed
Optimization algorithm},
booktitle = {Proc. 10th Int Hybrid Intelligent Systems (HIS) Conf},
year = {2010},
pages = {153--158},
abstract = {In this article we describe an <span class='snippet'>optimization</span>-based
design method for non-uniform, planar, and circular antenna arrays
<span class='snippet'>with</span> the objective of achieving minimum
side lobe levels for a specific first null beamwidth and also a minimum
size of the circumference. Central to our design is a hybridization
of two prominent metaheuristics of current interest namely the Invasive
Weed <span class='snippet'>Optimization</span> (IWO) and the Differential
Evolution (DE). IWO is a derivative-free real parameter <span class='snippet'>optimization</span>
technique that mimics the ecological behavior of colonizing weeds.
Owing to its superior performance in comparison <span class='snippet'>with</span>
many other existing metaheuristics, recently IWO is being used in
several <span class='snippet'>engineering</span> design problems
from diverse domains. For the present application, we have modified
classical IWO by incorporating the difference vector based mutation
schemes from the realm of DE. Three difficult instances of the circular
array design problem have been presented to illustrate the effectiveness
of the hybrid Differential IWO (DIWO) algorithm. The design results
obtained <span class='snippet'>with</span> modified IWO have been
shown to comfortably outperform the results obtained <span class='snippet'>with</span>
other state-of-the-art metaheuristics like <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO), and Differential Evolution (DE) in a statistically significant
fashion.},
doi = {10.1109/HIS.2010.5600021},
}
@inproceedings{Cai2010a,
author = {Jianghui Cai and Wenjun Meng},
title = {A New Parameters Optimization Method for Screw Conveyor Based on
PSO},
booktitle = {Proc. 3rd Int Emerging Trends in Engineering and Technology (ICETET)
Conf},
year = {2010},
pages = {263--266},
abstract = {<span class='snippet'>Particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) is a population based
stochastic <span class='snippet'>optimization</span> technique. As
a result, PSO algorithm is widely used in mechanical <span class='snippet'>engineering</span>
design field. Screw conveyors are used extensively in agriculture
and processing industries for elevating and/or transporting bulk
materials over short to medium distances. They are very effective
for conveying dry particulate solids, giving good control over the
throughput. Despite their apparent simplicity, the transportation
action is very complex and designers have tended to rely heavily
on empirical performance data. Intelligent operation parameters <span
class='snippet'>optimization</span> for screw conveyor based on PSO
is studied in this paper. This thesis takes a heavy driving drum
of screw conveyor as an example, firstly, the <span class='snippet'>optimization</span>
function is built up, then the operation parameter of screw conveyor
is precision optimized <span class='snippet'>with</span> PSO algorithm,
which offer a foundation to design more reasonable structure for
driving drum in order to meet the application demands.},
doi = {10.1109/ICETET.2010.141},
}
@inproceedings{Caputo2010,
author = {Caputo, D. and Grimaccia, F. and Mussetta, M. and Zich, R. E. },
title = {Photovoltaic plants predictive model by means of ANN trained by a
hybrid evolutionary algorithm},
booktitle = {Proc. Int Neural Networks (IJCNN) Joint Conf},
year = {2010},
pages = {1--6},
abstract = {This paper introduces a hybrid evolutionary <span class='snippet'>optimization</span>
algorithm as a tool for training an Artificial Neural Network used
for production forecasting of solar energy PV plants. This hybrid
technique is developed in order to exploit in the most effective
way the uniqueness and peculiarities of two classical <span class='snippet'>optimization</span>
approaches, <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (PSO) and Genetic Algorithms
(GA). This procedure essentially represent a bio-inspired heuristic
search technique, which can be used to solve combinatorial <span
class='snippet'>optimization</span> problems, modeled on the concepts
of natural selection and evolution (GA), but also based on cultural
and social behaviours derived from the analysis of the <span class='snippet'>swarm</span>
intelligence and interaction among <span class='snippet'>particles</span>
(PSO). Some simulation results are reported to highlight advantages
and drawbacks of the proposed technique in order to suitably apply
this algorithm to neural network applications in <span class='snippet'>engineering</span>
problems.},
doi = {10.1109/IJCNN.2010.5596782},
}
@inproceedings{Djuric2010e,
author = {Djuric, Petar M. },
title = {From nature to methods and back to nature},
booktitle = {Proc. Int Security and Cryptography (SECRYPT) Conf},
year = {2010},
abstract = {A fundamental challenge in today's arena of complex systems is the
design and development of accurate and robust signal processing methods.
These methods should be capable to adapt quickly to unexpected changes
in the data and operate under minimal model assumptions. Systems
in Nature also do signal processing and often do it optimally. Therefore,
it makes much sense to understand what Nature does and try to mimic
it and do even better. In return, the results of better signal processing
methods may lead to new advancements in science and technology and
in understanding Nature. In this presentation methods for signal
processing that borrow concepts and principles found in Nature are
addressed including ant <span class='snippet'>optimization</span>,
<span class='snippet'>swarm</span> intelligence and genetic algorithms.
However, the emphasis of the presentation is on Monte Carlo-based
methods, and in particular, methods related to <span class='snippet'>particle</span>
filtering, cost-reference <span class='snippet'>particle</span> filtering,
and population Monte Carlo. In the past decade and a half, Monte
Carlo-based methods have gained considerable popularity in dealing
<span class='snippet'>with</span> nonlinear and/or non-Gaussian systems.
The three groups of methods share the feature that they explore spaces
of unknowns using <span class='snippet'>particles</span> and weights
(costs) assigned to the <span class='snippet'>particles</span>. In
most versions of these methods, <span class='snippet'>particles</span>
move independently and in accordance <span class='snippet'>with</span>
the dynamics of the assumed model of the states. Interactions among
<span class='snippet'>particles</span> only occur through the process
of resampling rather than through local interactions as is common
in physical and biological systems. Such interactions can improve
the performance of the methods and can allow for coping <span class='snippet'>with</span>
more challenging problems <span class='snippet'>with</span> better
efficiency and accuracy. We show how we apply these methods to problems
in <span class='snippet'>engineering</span>, economics, and biology.},
}
@article{Goudos2010,
author = {Goudos, S. K. and Sahalos, J. N. },
title = {Pareto Optimal Microwave Filter Design Using Multiobjective Differential
Evolution},
journal = IEEE_J_AP,
year = {2010},
volume = {58},
pages = {132--144},
number = {1},
abstract = {Microwave filters play an important role in modern wireless communications.
A novel method for the design of multilayer dielectric and open loop
ring resonator (OLRR) filters under constraints is presented. The
proposed design method is based on generalized differential evolution
(GDE3), which is a multiobjective extension of differential evolution
(DE). GDE3 algorithm can be applied for global <span class='snippet'>optimization</span>
to any <span class='snippet'>engineering</span> problem <span class='snippet'>with</span>
an arbitrary number of objective and constraint functions. GDE3 is
compared against other evolutionary multiobjective algorithms like
nondominated sorting genetic algorithm-II (NSGA-II), multiobjective
<span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (MOPSO) and multiobjective
<span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> <span class='snippet'>with</span>
fitness sharing (MOPSO-fs) for a number of microwave filter design
cases. In the multilayer dielectric filter design case a predefined
database of low loss dielectric materials is used. The results indicate
the advantages of this approach and the applicability of this design
method.},
doi = {10.1109/TAP.2009.2032100},
}
@inproceedings{Jiang2010,
author = {Annan Jiang and Junxiang Wang and Jiao Zhang},
title = {Identifying the tunnel surrounding rock parameters based on particle
swarm optimization arithmetic},
booktitle = {Proc. Int Intelligent Control and Information Processing (ICICIP)
Conf},
year = {2010},
pages = {197--201},
abstract = {Because numerical model complexity of tunnel, the paper induces a
<span class='snippet'>swarm</span> intelligence <span class='snippet'>optimization</span>
algorithm - improved <span class='snippet'>particle</span> <span
class='snippet'>swarm</span> <span class='snippet'>optimization</span>,
constructs the surrounding rock parameters identification method.
Based on orthogonal design and uniformity design, the numerical tests
of underground <span class='snippet'>engineering</span> are carried
out, so as to produce data set to gain the regress model relating
rock mechanical parameters and monitoring displacements. Begin from
the random values of parameters, take error between calculation values
and monitoring data as fitness value, iterating according to the
PSO rule, then the parameters adaptive identification realized. True
tunnel <span class='snippet'>engineering</span> calculation states
that the mean has good global <span class='snippet'>optimization</span>
ability, the recognition accuracy is satisfied.},
doi = {10.1109/ICICIP.2010.5565303},
}
@article{Jin2010,
author = {Nanbo Jin and Rahmat-Samii, Y. },
title = {Hybrid Real-Binary Particle Swarm Optimization (HPSO) in Engineering
Electromagnetics},
journal = IEEE_J_AP,
year = {2010},
volume = {58},
pages = {3786--3794},
number = {12},
abstract = {The applications of a hybrid real-binary <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
(HPSO) algorithm in <span class='snippet'>engineering</span> electromagnetics
are described. In HPSO, each candidate design is designated by a
hybridized vector consisting of both real and binary variables. These
variables are evolved in the <span class='snippet'>optimization</span>
by following the velocity/position updating formulas of real-number
PSO (RPSO) and binary PSO (BPSO), respectively. Both single- and
multi-objective implementations of the algorithm are validated by
functional testbeds. Simulation and measurement results of three
examples, i.e, the design of a non-uniform antenna array, a multilayered
planar radar absorbing material (RAM) and a dual-band handset antenna
are presented, in order to illustrate the effectiveness of the algorithm
in representative topology exploration and material selection problems.},
doi = {10.1109/TAP.2010.2078477},
}
@inproceedings{Qiao2010,
author = {Zhihe Qiao and Hongyan Zhang},
title = {Research on Estimation Methods of Internal Rate of Return in Hydraulic
Engineering Project},
booktitle = {Proc. 2nd Int Information Engineering and Computer Science (ICIECS)
Conf},
year = {2010},
pages = {1--4},
abstract = {The internal rate of return method is currently an important analysis
tool for economic feasibility evaluation of <span class='snippet'>engineering</span>
project. Against the disadvantages of general methods to calculate
internal rate of return, such as big calculation amount, slow speed
and low result precision, the calculation of internal rate of return
in <span class='snippet'>engineering</span> project was equivalently
derived into an <span class='snippet'>optimization</span> problem.
Then <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> algorithm and ant colony
<span class='snippet'>optimization</span> algorithm were applied
to pursuit the optimal solution. A hydraulic <span class='snippet'>engineering</span>
project was taking as an example and the analysis results indicated
that the two methods used in the paper were simple, effective and
precise, which can be extensively used in <span class='snippet'>engineering</span>
projects.},
doi = {10.1109/ICIECS.2010.5678145},
}
@inproceedings{Wei2010,
author = {Kou Wei and Sun Feng-rui and Yang Li and Chen Lin-gen},
title = {Application of BCC Algorithm and RBFNN in Identification of Defect
Parameters},
booktitle = {Proc. Int Intelligence Information Processing and Trusted Computing
(IPTC) Symp},
year = {2010},
pages = {638--642},
abstract = {The identification of defect parameters in thermal non-destructive
test and evaluation (NDT/E) was considered as a kind of inverse heat
transfer problem (IHTP). However, it can be farther considered as
a shape optimization problem then a structure design optimization
problem, and the design results should meet the surface temperature
profile of the apparatus with defects. A bacterial colony chemotaxis
(BCC) optimization algorithm and a radial basis function neural network
(RBFNN) are applied to the thermal NDT/E for the identification of
defects parameters. The RBFNN is a precise and convenient surrogate
model for the time costly finite element computation, which obtains
the surface temperature with different defect parameters. The BCC
optimization algorithm is derivatively-free, and the convergence
speed is fast. This method is applied to a simple verification case
and the result is acceptable. The algorithm is also compared with
the particle <span class='snippet'>swarm</span> optimization (PSO)
algorithm, and the BCC algorithm can access the optimum with faster
speed.},
doi = {10.1109/IPTC.2010.15},
}
@inproceedings{Yan2010,
author = {Qiao Yan and ChangBin Wu and Songzhao Lv and MingLiang Bi},
title = {Displacement Back-Analysis of rock-fill dam based on particle swarm
optimization and genetic neural network algorithm},
booktitle = {Proc. Int Computer Application and System Modeling (ICCASM) Conf},
year = {2010},
volume = {5},
abstract = {In view of the complexity of Back Analysis of rock-fill material parameter,
this paper uses genetic algorithm <span class='snippet'>optimization</span>
BP neural network weights and threshold, simulated finite element
calculation of rockfill dam by genetic neural network, combined <span
class='snippet'>with</span> the theory of <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
algorithm, and has realized inverse analysis of <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
and genetic neural network. The results showed that: the simulation
result coincides well <span class='snippet'>with</span> the experimental
result, and inversion results could meet the computing requirements.
This method can be widely used to solve various complicated <span
class='snippet'>engineering</span> problems that target functions
can't be expressed by apparent functions of decisive variables.},
doi = {10.1109/ICCASM.2010.5620126},
}
@inproceedings{Zhu2010a,
author = {Yan-fei Zhu and Xiong-min Tang},
title = {Overview of swarm intelligence},
booktitle = {Proc. Int Computer Application and System Modeling (ICCASM) Conf},
year = {2010},
volume = {9},
abstract = {This paper gives a broad overview of <span class='snippet'>swarm</span>
<span class='snippet'>intelligence</span> in three parts: biological
basis, artificial literature and <span class='snippet'>swarm</span>
<span class='snippet'>engineering</span>. In biological basis part,
the paper gives some operational principles from biological systems
by naturalists and biologists. In artificial literature part, two
fundamental approaches are provided to analyze <span class='snippet'>swarm</span>
topology. The prevalent <span class='snippet'>swarm</span> models
and techniques such as Reynolds's rules, discrete and continuum theory
of flocking, coordination stability of the <span class='snippet'>swarm</span>
motion, etc, are also summarized in this part. In <span class='snippet'>swarm</span>
<span class='snippet'>engineering</span> part, the paper discusses
Kazadi's &#x201C;two-step&#x201D; process. Many <span class='snippet'>engineering</span>
applications come from Kazadi's researches. Also, the main application
of <span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
on robot systems and other applications are introduced in this part.
We say this paper provides concepts for a better understanding of
<span class='snippet'>swarm</span> <span class='snippet'>intelligence</span>
both in principles and in applications.},
doi = {10.1109/ICCASM.2010.5623005},
}
@inproceedings{Hu2009,
author = {Min Hu and Fang-Fang Wu},
title = {Improved multi-classes SVM and application in ring-key position selection
in tunnels},
booktitle = {Proc. Int Machine Learning and Cybernetics Conf},
year = {2009},
volume = {2},
pages = {1094--1099},
abstract = {The universal trapezoidal tapered ring has been widespread applied
in the tunnel <span class='snippet'>engineering</span>. However,
it is difficult to select rational ring-key position location. As
it can be defined as a restrictive multi-classes problem, traditional
method cannot be used directly. This paper proposed a novel method,
CBPSO-RMCSVM, which combines chaos binary <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>
(CBPSO) <span class='snippet'>with</span> restrictive multi-classes
support vector machine (RMCSVM), so as to satisfied actual <span
class='snippet'>engineering</span> demand and improve the accuracy
and efficiency. <span class='snippet'>With</span> the guidance of
CBPSO-RMCSVM, a new decision support system of universal trapezoidal
tapered ring-key position selection in shield tunneling was designed
and developed. The system was applied to the Shanghai Yangtze River
tunnel project successfully, and the <span class='snippet'>engineering</span>
result showed the improved method had good performance.},
doi = {10.1109/ICMLC.2009.5212404},
}
@inproceedings{Korani2009,
author = {Korani, W. M. and Dorrah, H. T. and Emara, H. M. },
title = {Bacterial foraging oriented by Particle Swarm Optimization strategy
for PID tuning},
booktitle = {Proc. IEEE Int Computational Intelligence in Robotics and Automation
(CIRA) Symp},
year = {2009},
pages = {445--450},
abstract = {Proportional integral derivative (PID) controller tuning is an area
of interest for researchers in many disciplines of science and <span
class='snippet'>engineering</span>. This paper presents a new algorithm
for PID controller tuning based on a combination of the foraging
behavior of E coli bacteria foraging and <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO). The E coli algorithm depends on random search directions which
may lead to delay in reaching the global solution. The PSO algorithm
may lead to possible entrapment in local minimum solutions. This
paper proposed a new algorithm Bacteria Foraging oriented by PSO
(BF-PSO). The new algorithm is proposed to combines both algorithms'
advantages in order to get better <span class='snippet'>optimization</span>
values. The proposed algorithm is applied to the problem of PID controller
tuning and is compared <span class='snippet'>with</span> conveniently
Bacterial Foraging algorithm and <span class='snippet'>Particle</span>
<span class='snippet'>swarm</span> <span class='snippet'>optimization</span>.},
doi = {10.1109/CIRA.2009.5423165},
}
@inproceedings{Modarres2009,
author = {Modarres, H. and Alfi, A. },
title = {A particle Swarm Optimization approach for parameter identification
of Lorenz chaotic system},
booktitle = {Proc. 35th Annual Conf. of IEEE Industrial Electronics IECON '09},
year = {2009},
pages = {3303--3308},
abstract = {An important problem in <span class='snippet'>engineering</span> is
the identification of nonlinear systems, among them chaotic systems
have received particular attention due to their complex and unpredictable
behaviors. In this paper, a <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(PSO) technique is applied for online parameter identification of
Lorenz chaotic system. The difficulties of online implementation
mainly come from the unavoidable computational time to find a solution.
Due to this, first an Improved <span class='snippet'>Particle</span>
<span class='snippet'>Swarm</span> <span class='snippet'>Optimization</span>
(IPSO) is proposed to increase the convergence speed and accuracy
of the Standard <span class='snippet'>Particle</span> <span class='snippet'>Swarm</span>
<span class='snippet'>Optimization</span> (SPSO) to save tremendous
computation time. Second, IPSO is also improved to detect and determine
the variation of parameters. Finally, a numerical example is given
to verify the effectiveness of the proposed method compared to Genetic
Algorithm (GA) and SPSO.},
doi = {10.1109/IECON.2009.5415058},
}
@inproceedings{Wang2009,
author = {Lei Wang and Yuwen Zhou and Weiwei Zhao},
title = {Comparative Study on Bionic Optimization Algorithms for Sewer Optimal
Design},
booktitle = {Proc. Fifth Int. Conf. Natural Computation ICNC '09},
year = {2009},
volume = {3},
pages = {24--29},
abstract = {Sewer network as a necessary urban infrastructure plays an important
role in people's daily life. Conventional <span class='snippet'>optimization</span>
techniques have significant limitations on solving the problems of
sewer optimal design. Because as a high-dimensional discrete complex
<span class='snippet'>optimization</span> problem, sewer optimal
design is characterized by its discrete objective function and, as
an integer discrete variable, its decision variable amount keeps
the same pace <span class='snippet'>with</span> <span class='snippet'>engineering</span>
scales. Over the last decade, various kinds of modern bionic <span
class='snippet'>optimization</span> algorithms <span class='snippet'>with</span>
their special advantages have been created and applied into sewer
optimal design successfully. Based on previous studies, this paper
analyses and compares the solution performances of genetic algorithms
(GA), <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
<span class='snippet'>optimization</span> (PSO) and ant colony algorithms
(ACA) from the three aspects respectively, they are convergence,
speed and complexity of algorithm. The research result shows that
compared <span class='snippet'>with</span> the other two algorithms,
the ACA manifests its superiority for better convergence, satisfactory
speed and relatively small algorithm complexity, which are very suitable
for solving the problems of sewer optimal design.},
doi = {10.1109/ICNC.2009.89},
}
@inproceedings{Wu2009,
author = {Liu Wu and Liu Yi and Gu Xue-qin and Wang Deng-hai},
title = {Optimum Distribution of Aseismic Engineering Investment for Gas Pipeline
Network System Based on Improved Particle Swarm Algorithm},
booktitle = {Proc. Second Int. Conf. Intelligent Computation Technology and Automation
ICICTA '09},
year = {2009},
volume = {3},
pages = {249--252},
abstract = {Under the limit of <span class='snippet'>engineering</span> investment,
the aseismic optimum design of pipeline system is used to minimize
the sum of aseismic cost. Since optimal problem is an nonlinear hybrid
model which involves dual "curse of dimensionality" during the mathematical
programming and system reliability calculation, traditional optimal
techniques can hardly solve the problems of distribution of aseismic
<span class='snippet'>engineering</span> investment for lifeline
network system. Evolutionary-based <span class='snippet'>particle</span>
<span class='snippet'>swarm</span> algorithm has been investigated
and the improved design of inertia weight has been fulfilled in this
paper. A new hybrid <span class='snippet'>particle</span> <span class='snippet'>swarm</span>
algorithm makes use of the ergodicity of chaos to improve the capability
of precise search and keep the balance between the global search
and the local search. Combining <span class='snippet'>with</span>
Boolean cubical matrix disjoint calculation method used as aseismic
reliability analysis tool of network system, the improved algorithm
is applied to the <span class='snippet'>optimization</span> solution
of investment distribution for gas pipeline network .The analysis
of an example in a certain pipeline network system demonstrates that
the established model and algorithm can achieve better convergent
performance and speed.},
doi = {10.1109/ICICTA.2009.527},
}
@inproceedings{Zeng2009,
author = {Junwei Zeng and Li Wang and Tingliang Wang and Wenzhuo Fan and Hui
Gao},
title = {Particle swarm optimization-based machine arrangement for filling
construction of rock-fill dams},
booktitle = {Proc. IEEE Int. Conf. Industrial Engineering and Engineering Management
IEEM 2009},
year = {2009},
pages = {1772--1775},
abstract = {The factors that can influence the equipment <span class='snippet'>optimization</span>
in filling construction of rock-fill dam are explained by analyzing
the filling system of rock-fill dams in this paper, and an <span
class='snippet'>optimization</span> model for equipment organization
of fill construction of rock-fill dam is established, in which the
minimum of unbalanced degree of filling intensity is taken as the
objective function <span class='snippet'>with</span> constraint.
The quantum discrete <span class='snippet'>particle</span> <span
class='snippet'>swarm</span> <span class='snippet'>optimization</span>
is used in the model solution. Based on the <span class='snippet'>optimization</span>
model and algorithm, the hydraulic <span class='snippet'>engineering</span>
construction management decision support system is developed by using
an object-oriented methodology. The system is applied to a practical
project successfully to obtain the optimum solution of equipment
organization.},
doi = {10.1109/IEEM.2009.5373163},
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: "swarm intelligence"
% @cited: 4160
% @topic: overview
% @via: scholar.google.de
% @restrictions: commercial only
% @relevance: high
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@incollection{kennedy_swarm_2011,
address = {Boston},
title = {Swarm Intelligence},
isbn = {0-387-40532-1},
url = {http://www.springerlink.com/content/q528q1743224q233/},
booktitle = {Handbook of {Nature-Inspired} and Innovative Computing},
publisher = {Kluwer Academic Publishers},
author = {Kennedy, James},
editor = {Zomaya, Albert Y.},
month = nov,
year = {2011},
pages = {187--219}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: "swarm intelligence survey"
% @cited: 52
% @via: scholar.google.de
% @topic: data mining
% @relevance: medium
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{martens_editorial_2010,
title = {Editorial survey: swarm intelligence for data mining},
volume = {82},
issn = {0885-6125, 1573-0565},
shorttitle = {Editorial survey},
url = {http://www.springerlink.com/content/rmg323605m2uu817/},
doi = {10.1007/s10994-010-5216-5},
journal = {Machine Learning},
author = {Martens, David and Baesens, Bart and Fawcett, Tom},
month = sep,
year = {2010},
pages = {1--42}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: "swarm intelligence" (year restriction to 2008)
% @via: scholar.google.de
% @cited: 52
% @topic: algorithn
% @relevance: medium
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{karaboga_survey:_2009,
title = {A survey: algorithms simulating bee swarm intelligence},
volume = {31},
issn = {0269-2821, 1573-7462},
shorttitle = {A survey},
url = {http://www.springerlink.com/content/500886349544k37u/},
doi = {10.1007/s10462-009-9127-4},
journal = {Artificial Intelligence Review},
author = {Karaboga, Dervis and Akay, Bahriye},
month = oct,
year = {2009},
pages = {61--85}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: "swarm OR collective intelligence visual detection"
% @cited: 14
% @topic: computer vision
% @via: scholar.google.de
% @relevance: high (preferred)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{owechko_cognitive_2005,
title = {Cognitive swarms for rapid detection of objects and associations in visual imagery},
isbn = {0-7803-8916-6},
doi = {10.1109/SIS.2005.1501656},
abstract = {We have developed a new optimization-based framework for computer vision that combines ideas from particle swarm optimization {(PSO)} and statistical pattern recognition to rapidly and accurately detect and classify objects in visual imagery. Swarm intelligence is used to locate objects by optimizing the classification confidence level. We have used our cognitive swarm framework to rapidly detect people, ground vehicles, and boats, and to recognize behaviors based on object associations, such as people exiting and entering vehicles, for applications in security, surveillance, target recognition, and automotive active safety.},
booktitle = {Proceedings 2005 {IEEE} Swarm Intelligence Symposium, 2005. {SIS} 2005},
publisher = {{IEEE}},
author = {Owechko, Y. and Medasani, S.},
month = jun,
year = {2005},
keywords = {Boats, cognition, cognitive swarms, computer vision, image classification, Land vehicles, object classification, object detection, particle swarm optimisation, Particle swarm optimization, Pattern recognition, Road vehicles, statistical analysis, statistical pattern recognition, Swarm intelligence, Target recognition, Vehicle detection, Vehicle safety, visual imagery},
pages = {420-- 423}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: "swarm OR collective intelligence ad hoc network"
% @cited: 14
% @topic: network/routing
% @via: scholar.google.de
% @relevance: high (preferred)
% @note: looks very interresting; long paper; (nice pictures;)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{shen_ad_2005,
title = {Ad hoc multicast routing algorithm with swarm intelligence},
volume = {10},
issn = {{1383-469X}},
url = {http://dx.doi.org/10.1145/1046430.1046435},
doi = {10.1145/1046430.1046435},
number = {1-2},
journal = {Mob. Netw. Appl.},
author = {Shen, {Chien-Chung} and Jaikaeo, Chaiporn},
month = feb,
year = {2005},
keywords = {ad hoc networks, multicast routing, Swarm intelligence},
pages = {47–59}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: "swarm intelligence robotics"
% @cited: 113
% @topic: robotics
% @via: scholar.google.de
% @relevance: medium
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@incollection{ahin_swarm_2005,
address = {Berlin, Heidelberg},
title = {From Swarm Intelligence to Swarm Robotics},
volume = {3342},
isbn = {978-3-540-24296-3, 978-3-540-30552-1},
url = {http://www.springerlink.com/content/xn02ymel1b22rjp8/},
booktitle = {Swarm Robotics},
publisher = {Springer Berlin Heidelberg},
author = {Beni, Gerardo},
editor = {Şahin, Erol and Spears, William M.},
year = {2005},
pages = {1--9}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: ""collective intelligence" OR "swarm intelligence" collision "
% @cited: 74
% @topic: collision detection
% @via: scholar.google.de
% @relevance: high (preferred)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{blackwell_dont_2002,
title = {Don't push me! Collision-avoiding swarms},
volume = {2},
isbn = {0-7803-7282-4},
doi = {10.1109/CEC.2002.1004497},
abstract = {This paper examines a particle swarm algorithm which has been applied to the generation of interactive, improvised music. An important feature of this algorithm is a balance between particle attraction to the centre of mass and repulsive, collision avoiding forces. These forces are not present in the classic particle swarm optimisation algorithms. A number of experiments illuminate the nature of these new forces and it is suggested that the algorithm may have applications to dynamic optimisation problems},
booktitle = {Proceedings of the 2002 Congress on Evolutionary Computation, 2002. {CEC} '02},
publisher = {{IEEE}},
author = {Blackwell, T. M and Bentley, P.},
year = {2002},
keywords = {Acceleration, Application software, Coherence, collision avoiding forces, collision-avoiding swarms, Computer science, dynamic optimisation, Educational institutions, evolutionary computation, improvised music, Music, optimisation, particle attraction, particle swarm algorithm, Particle swarm optimization, Particle tracking, Shape, Target tracking},
pages = {1691--1696}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: ""swarm intelligence" routing"
% @cited: 88
% @topic: network/routing
% @via: scholar.google.de
% @relevance: high
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{kassabalidis_swarm_2001,
title = {Swarm intelligence for routing in communication networks},
volume = {6},
isbn = {0-7803-7206-9},
doi = {10.1109/GLOCOM.2001.966355},
abstract = {Swarm intelligence, as demonstrated by natural biological swarms, exhibits numerous powerful features that are desirable in many engineering systems, such as communication networks. In addition, new paradigms for designing autonomous and scalable systems may result from analytically understanding and extending the design principles and operations inherent in intelligent biological swarms. A key element of future design paradigms will be emergent intelligence - simple local interactions of autonomous swarm members, with simple primitives, giving rise to complex and intelligent global behavior. Communication network management is becoming increasingly difficult due to the increasing network size, rapidly changing topology, and complexity. A new class of algorithms, inspired by swarm intelligence, is currently being developed that can potentially solve numerous problems of such networks. These algorithms rely on the interaction of a multitude of simultaneously interacting agents. A survey of such algorithms and their performance is presented here},
booktitle = {{IEEE} Global Telecommunications Conference, 2001. {GLOBECOM} '01},
publisher = {{IEEE}},
author = {Kassabalidis, I. and {El-Sharkawi}, M. A and Marks, R. J. and Arabshahi, P. and Gray, A. A},
year = {2001},
keywords = {autonomous scalable systems, Bandwidth, communication network management, Communication networks, Delay, emergent intelligence, Intelligent agent, intelligent biological swarms, intelligent global behavior, Intelligent networks, mobile radio, network complexity, network topology, packet radio networks, Packet switching, Particle swarm optimization, performance, Power engineering and energy, quality of service, Routing, simultaneously interacting agents, Swarm intelligence, telecommunication network management, telecommunication network routing, Throughput},
pages = {3613--3617 vol.6}
},
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% @keyword: ""collective intelligence" routing"
% @cited: 87
% @topic: network/routing
% @via: scholar.google.de
% @relevance: high (preferred)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{wolpert_using_1999,
title = {Using Collective Intelligence to Route Internet Traffic},
url = {http://arxiv.org/abs/cs/9905004},
abstract = {A {COllective} {INtelligence} {(COIN)} is a set of interacting reinforcement learning {(RL)} algorithms designed in an automated fashion so that their collective behavior optimizes a global utility function. We summarize the theory of {COINs}, then present experiments using that theory to design {COINs} to control internet traffic routing. These experiments indicate that {COINs} outperform all previously investigated {RL-based}, shortest path routing algorithms.},
journal = {{arXiv:cs/9905004}},
author = {Wolpert, David H and Tumer, Kagan and Frank, Jeremy},
month = may,
year = {1999},
note = {Advances in Information Processing Systems - 11, eds M. Kearns, S. Solla, D. Cohn, {MIT} Press, 1999},
keywords = {Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Learning, Computer Science - Networking and Internet Architecture, Condensed Matter - Statistical Mechanics, I.2.11, I.2.6, Nonlinear Sciences - Adaptation and {Self-Organizing} Systems}
}
Jump to Line
Something went wrong with that request. Please try again.