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Evolutionary Computation Bestiary

DOI

Updated 2021-05-04

"Till now, madness has been thought a small island in an ocean of sanity. I am beginning to suspect that it is not an island at all but a continent." -- Machado de Assis, The Psychiatrist.

Introduction

The field of meta-heuristic search algorithms has a long history of finding inspiration in natural systems. Starting from classics such as Genetic Algorithms and Ant Colony Optimization, the last two decades have witnessed a fireworks-style explosion (pun intended) of natural (and sometimes supernatural) heuristics - from Birds and Bees to Zombies and Reincarnation.

The goal of the Evolutionary Computation Bestiary is to catalog the, ermm... exuberance of the meta-heuristic "eco-system". We try to keep a list of the many different animals, plants, microbes, natural phenomena and supernatural activities that can be spotted in the wild lands of the metaphor-based computation literature.

While we personally believe that the literature could do with more mathematics and less marsupials, and that we, as a community, should grow past this metaphor-rich phase in our field's history (a bit like chemistry outgrew alchemy), please note that this list makes no claims about the scientific quality of the papers listed. The EC Bestiary puts classic works of the metaheuristics literature (e.g., GAs, ACO) and some that describe their methods in mostly metaphor-free language (e.g., JTF, CFO) side by side with others for which the scientific rigor is, to put it mildly, lacking. In short, it is not a Hall of Fame of algorithms - think of it more as The island of Doctor Moreau: a place with a few good creatures, but which are vastly outnumbered by mindless beasts.

Finally, if you know a metaphor-based method that is not listed here, or if you know of an earlier mention of a listed method, please see the bottom of the page on how to contribute!

The Bestiary

BioHeuristics GO

A

  • African Buffalo: Odili JB, Kahar MNM (2016). “Solving the Traveling Salesman's Problem Using the African Buffalo Optimization.” Computational Intelligence and Neuroscience, 2016, 1-12. doi: 10.1155/2016/1510256
  • Algae: Uymaz SA, Tezel G, Yel E (2015). “Artificial algae algorithm (AAA) for nonlinear global optimization.” Applied Soft Computing, 31, 153-171. doi: 10.1016/j.asoc.2015.03.003
  • Amoeba: Wang H, Lu X, Zhang X, Wang Q, Deng Y (2014). “A Bio-Inspired Method for the Constrained Shortest Path Problem.” The Scientific World Journal, 2014, 1-11. doi: 10.1155/2014/271280
  • Amoeba: Plasmodium: Zhu L, Kim S, Hara M, Aono M (2018). “Remarkable problem-solving ability of unicellular amoeboid organism and its mechanism.” Royal Society Open Science, 5(12), 180396. doi: 10.1098/rsos.180396
  • Anarchic Society: Shayeghi H, Dadashpour J (2012). “Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System.” Electrical and Electronic Engineering, 2(4), 199-207. doi: 10.5923/j.eee.20120204.05
  • Andean Condors: Almonacid B, Soto R (2018). “Andean Condor Algorithm for cell formation problems.” Natural Computing. doi: 10.1007/s11047-018-9675-0
  • Anglerfish: Pook MF, Ramlan EI (2018). “The Anglerfish algorithm: a derivation of randomized incremental construction technique for solving the traveling salesman problem.” Evolutionary Intelligence, 12(1), 11-20. doi: 10.1007/s12065-018-0169-x
  • Animal Behavior: Hunting: Naderi B, Khalili M, Khamseh AA (2014). “Mathematical models and a hunting search algorithm for the no-wait flowshop scheduling with parallel machines.” International Journal of Production Research, 52(9), 2667-2681. doi: 10.1080/00207543.2013.871389
  • Animal Behavior: Predation: Tilahun SL, Ong HC (2015). “Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems.” International Journal of Information Technology & Decision Making, 14(06), 1331-1352. doi: 10.1142/s021962201450031x
  • Animal Behavior: Searching: He S, Wu Q, Saunders J (2009). “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior.” IEEE Transactions on Evolutionary Computation, 13(5), 973-990. doi: 10.1109/tevc.2009.2011992
  • Ant Colony: Maniezzo A (1992). “Distributed optimization by ant colonies.” In Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, 134. Mit Press.
  • Ant Lion: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: 10.1016/j.advengsoft.2015.01.010
  • Antibodies: De Castro LN, Von Zuben FJ (2000). “The clonal selection algorithm with engineering applications.” In Proceedings of GECCO, volume 2000, 36-39.
  • Artillery: Pijarski P, Kacejko P (2019). “A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG).” Engineering Optimization, 51(12), 2049-2068. doi: 10.1080/0305215x.2019.1565282

B

  • Bachelors: Hu TC, Kahng AB, Tsao CA (1995). “Old Bachelor Acceptance: A New Class of Non-Monotone Threshold Accepting Methods.” ORSA Journal on Computing, 7(4), 417-425. doi: 10.1287/ijoc.7.4.417
  • Bacteria: Bacterial Chemotaxis: Muller S, Marchetto J, Airaghi S, Koumoutsakos P (2002). “Optimization based on bacterial chemotaxis.” IEEE Transactions on Evolutionary Computation, 6(1), 16-29. doi: 10.1109/4235.985689
  • Bacteria: Bacterial Foraging: Passino K (2002). “Biomimicry of bacterial foraging for distributed optimization and control.” IEEE Control Systems Magazine, 22(3), 52-67. doi: 10.1109/mcs.2002.1004010
  • Bacteria: Bacterial Swarming: Chu Y, Mi H, Liao H, Ji Z, Wu QH (2008). “A Fast Bacterial Swarming Algorithm for high-dimensional function optimization.” In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). doi: 10.1109/cec.2008.4631222
  • Bacteria: Magnetotactic Bacteria: Mo H, Xu L (2013). “Magnetotactic bacteria optimization algorithm for multimodal optimization.” In 2013 IEEE Symposium on Swarm Intelligence (SIS). doi: 10.1109/sis.2013.6615185
  • Barnacles Mating: Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Mohamad AJ, Othman MR, Mohamed MR (2019). “Barnacles Mating Optimizer Algorithm for Optimization.” In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 211-218. Springer Singapore. doi: 10.1007/978-981-13-3708-6_18
  • Bats: Yang X (2010). “A new metaheuristic bat-inspired algorithm.” In Nature inspired cooperative strategies for optimization (NICSO 2010), 65-74. Springer.
  • Battle Royale Game: Farshi TR (2020). “Battle royale optimization algorithm.” Neural Computing and Applications, 33(4), 1139-1157. doi: 10.1007/s00521-020-05004-4
  • Bees: Bee Colonies: Teodorovic D, Lucic P, Markovic G, Orco MD (2006). “Bee Colony Optimization: Principles and Applications.” In 2006 8th Seminar on Neural Network Applications in Electrical Engineering. doi: 10.1109/neurel.2006.341200
  • Bees: Bee Colonies 2: Karaboga D, Basturk B (2007). “Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems.” In Lecture Notes in Computer Science, 789-798. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-72950-1_77
  • Bees: Bumblebees: Comellas F, Martinez-Navarro J (2009). “Bumblebees.” In Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC \textquotesingle09. doi: 10.1145/1543834.1543949
  • Bees: Honey Bee Marriages: Abbass H (2001). “MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach.” In Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546). doi: 10.1109/cec.2001.934391
  • Bees: Queen Bees: Jung SH (2003). “Queen-bee evolution for genetic algorithms.” Electronics Letters, 39(6), 575. doi: 10.1049/el:20030383
  • Beetles: Kallioras NA, Lagaros ND, Avtzis DN (2018). “Pity beetle algorithm \textendash A new metaheuristic inspired by the behavior of bark beetles.” Advances in Engineering Software, 121, 147-166. doi: 10.1016/j.advengsoft.2018.04.007
  • Big Bang: Erol OK, Eksin I (2006). “A new optimization method: Big Bang\textendashBig Crunch.” Advances in Engineering Software, 37(2), 106-111. doi: 10.1016/j.advengsoft.2005.04.005
  • Biogeography: Simon D (2008). “Biogeography-Based Optimization.” IEEE Transactions on Evolutionary Computation, 12(6), 702-713. doi: 10.1109/tevc.2008.919004
  • Birds: Bird Migrations: Duman E, Uysal M, Alkaya AF (2012). “Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem.” Information Sciences, 217, 65-77. doi: 10.1016/j.ins.2012.06.032
  • Birds: Birds Mating: Askarzadeh A (2014). “Bird mating optimizer: An optimization algorithm inspired by bird mating strategies.” Communications in Nonlinear Science and Numerical Simulation, 19(4), 1213-1228. doi: 10.1016/j.cnsns.2013.08.027
  • Birds: Hitchcock Birds: Morais RG, Nedjah N, Mourelle LM (2019). “A novel metaheuristic inspired by Hitchcock birds' behavior for efficient optimization of large search spaces of high dimensionality.” Soft Computing, 24(8), 5633-5655. doi: 10.1007/s00500-019-04102-3
  • Birds: Micro Migration: Gao L, Pan Q (2016). “A shuffled multi-swarm micro-migrating birds optimizer for a multi-resource-constrained flexible job shop scheduling problem.” Information Sciences, 372, 655-676. doi: 10.1016/j.ins.2016.08.046
  • Bison: Kazikova A, Pluhacek M, Senkerik R, Viktorin A (2018). “Proposal of a New Swarm Optimization Method Inspired in Bison Behavior.” In Recent Advances in Soft Computing, 146-156. Springer International Publishing. doi: 10.1007/978-3-319-97888-8_13
  • Black Holes: Hatamlou A (2013). “Black hole: A new heuristic optimization approach for data clustering.” Information Sciences, 222, 175-184. doi: 10.1016/j.ins.2012.08.023
  • Black Widow: Hayyolalam V, Kazem AAP (2020). “Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems.” Engineering Applications of Artificial Intelligence, 87, 103249. doi: 10.1016/j.engappai.2019.103249
  • Blind Naked Mole Rats: Taherdangkoo M, Shirzadi MH, Yazdi M, Bagheri MH (2013). “A robust clustering method based on blind, naked mole-rats (BNMR) algorithm.” Swarm and Evolutionary Computation, 10, 1-11. doi: 10.1016/j.swevo.2013.01.001
  • Bonobos: Das AK, Nikum AK, Krishnan SV, Pratihar DK (2020). “Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization.” Knowledge and Information Systems, 62(11), 4407-4444. doi: 10.1007/s10115-020-01503-x
  • Brainstorming: Shi Y (2011). “An Optimization Algorithm Based on Brainstorming Process.” International Journal of Swarm Intelligence Research, 2(4), 35-62. doi: 10.4018/ijsir.2011100103
  • BrunsVigia Flower: Ghaemidizaji M, Dadkhah C, Leung H (2018). “A New Optimization Algorithm Based on the Behavior of BrunsVigia Flower.” In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). doi: 10.1109/smc.2018.00055
  • Buses: Bodaghi M, Samieefar K (2018). “Meta-heuristic bus transportation algorithm.” Iran Journal of Computer Science. doi: 10.1007/s42044-018-0025-2
  • Butterflies: Monarch Butterflies: Wang G, Deb S, Cui Z (2015). “Monarch butterfly optimization.” Neural Computing and Applications. doi: 10.1007/s00521-015-1923-y
  • Butterflies: Regular Butterflies: Arora S, Singh S (2018). “Butterfly optimization algorithm: a novel approach for global optimization.” Soft Computing. doi: 10.1007/s00500-018-3102-4
  • Buzzards: Arshaghi A, Ashourian M, Ghabeli L (2019). “Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm.” Majlesi Journal of Electrical Engineering, 13(3), 83-98. <URL: http://mjee.iaumajlesi.ac.ir/index/index.php/ee/article/view/3363>.

C

  • Camels: M. K. Ibrahim RSA (2016). “Novel Optimization Algorithm Inspired by Camel Traveling Behavior.” Iraq J. Electrical and Electronic Engineering, 12(2), 167-177. ISSN 18145892, <URL: https://www.iasj.net/iasj?func=article&aId=118375>.
  • Cancers: Tang D, Dong S, Jiang Y, Li H, Huang Y (2015). “ITGO: Invasive tumor growth optimization algorithm.” Applied Soft Computing, 36, 670-698. doi: 10.1016/j.asoc.2015.07.045
  • Cats: Chu S, Tsai P, Pan J (2006). “Cat Swarm Optimization.” In Lecture Notes in Computer Science, 854-858. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-36668-3_94
  • Central Force: Formato RA (2007). “CENTRAL FORCE OPTIMIZATION: A NEW METAHEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS.” Progress In Electromagnetics Research, 77, 425-491. doi: 10.2528/pier07082403
  • Chameleons: Braik MS (2021). “Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems.” Expert Systems with Applications, 174, 114685. doi: 10.1016/j.eswa.2021.114685
  • Charged Systems: Kaveh A, Talatahari S (2010). “A novel heuristic optimization method: charged system search.” Acta Mechanica, 213(3-4), 267-289. doi: 10.1007/s00707-009-0270-4
  • Cheetah: Klein CE, Mariani V, dos Santos Coelho L (2018). “Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm.” In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
  • Chemical Reactions: Alatas B (2011). “ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization.” Expert Systems with Applications, 38(10), 13170-13180. doi: 10.1016/j.eswa.2011.04.126
  • Chickens: Chicken Laying Eggs: Hosseini E (2017). “Laying Chicken Algorithm: A New Meta-Heuristic Approach to Solve Continuous Programming Problems.” Journal of Applied & Computational Mathematics, 06(01). doi: 10.4172/2168-9679.1000344
  • Chickens: Chicken Swarms: Meng X, Liu Y, Gao X, Zhang H (2014). “A New Bio-inspired Algorithm: Chicken Swarm Optimization.” In Lecture Notes in Computer Science, 86-94. Springer International Publishing. doi: 10.1007/978-3-319-11857-4_10
  • Clouds: YAN G, HAO Z (2013). “A NOVEL OPTIMIZATION ALGORITHM BASED ON ATMOSPHERE CLOUDS MODEL.” International Journal of Computational Intelligence and Applications, 12(01), 1350002. doi: 10.1142/s1469026813500028
  • Cockroaches: Obagbuwa IC, Adewumi AO (2014). “An Improved Cockroach Swarm Optimization.” The Scientific World Journal, 2014, 1-13. doi: 10.1155/2014/375358
  • Colliding Bodies: Kaveh A, Mahdavi V (2014). “Colliding bodies optimization: A novel meta-heuristic method.” Computers & Structures, 139, 18-27. doi: 10.1016/j.compstruc.2014.04.005
  • Community of scientists: Alfredo M, Valentino S (2012). “Community of scientist optimization: An autonomy oriented approach to distributed optimization.” AI Communications, 25(2), 157–172. ISSN 0921-7126, doi: 10.3233/AIC-2012-0526
  • Consultants: Iordache S (2010). “Consultant-guided search.” In Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO \textquotesingle10. doi: 10.1145/1830483.1830526
  • Coral Reefs: Salcedo-Sanz S, Ser JD, Landa-Torres I, Gil-López S, Portilla-Figueras JA (2014). “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems.” The Scientific World Journal, 2014, 1-15. doi: 10.1155/2014/739768
  • COVID19: Hosseini E, Ghafoor KZ, Sadiq AS, Guizani M, Emrouznejad A (2020). “COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process.” IEEE Journal of Biomedical and Health Informatics, 24(10), 2765-2775. doi: 10.1109/jbhi.2020.3012487
  • Coyotes: Pierezan J, Coelho LDS (2018). “Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems.” In 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8. IEEE.
  • Crows: Askarzadeh A (2016). “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm.” Computers & Structures, 169, 1-12. doi: 10.1016/j.compstruc.2016.03.001
  • Crows: Chaotic: Rizk-Allah RM, Hassanien AE, Bhattacharyya S (2018). “Chaotic crow search algorithm for fractional optimization problems.” Applied Soft Computing, 71, 1161-1175. doi: 10.1016/j.asoc.2018.03.019
  • Crystal Energy: Feng X, Ma M, Yu H (2014). “Crystal Energy Optimization Algorithm.” Computational Intelligence, 32(2), 284-322. doi: 10.1111/coin.12053
  • Cuckoos: Yang X, Deb S (2009). “Cuckoo Search via L&#x00E9$\mathsemicolon$vy flights.” In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). doi: 10.1109/nabic.2009.5393690

D

  • Deer: Scottish Red Deer: Fard AF, Hajiaghaei-Keshteli M (2016). “Red Deer Algorithm (RDA); A New Optimization Algorithm Inspired by Red Deers’ Mating.” In International Conference on Industrial Engineering, IEEE.,(2016 e), 33-34.
  • Dendritic Cells: Greensmith J, Aickelin U, Cayzer S (2005). “Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection.” In International Conference on Artificial Immune Systems, 153-167. Springer.
  • Dice Games: DEHGHANI M, MONTAZERI Z, MALIK OP (2019). “DGO: Dice Game Optimizer.” GAZI UNIVERSITY JOURNAL OF SCIENCE, 32(3), 871-882. doi: 10.35378/gujs.484643
  • Dogs: Subramanian C, Sekar A, Subramanian K (2013). “A New Engineering Optimization Method: African Wild Dog Algorithm.” International Journal of Soft Computing, 8(3).
  • Dogs: Border Collie: Dutta T, Bhattacharyya S, Dey S, Platos J (2020). “Border Collie Optimization.” IEEE Access, 8, 109177-109197. doi: 10.1109/access.2020.2999540
  • Dolphins: Dolphin Echolocation: Kaveh A, Farhoudi N (2013). “A new optimization method: Dolphin echolocation.” Advances in Engineering Software, 59, 53-70. doi: 10.1016/j.advengsoft.2013.03.004
  • Dolphins: Dolphin Partners: Shiqin Y, Jianjun J, Guangxing Y (2009). “A Dolphin Partner Optimization.” In 2009 WRI Global Congress on Intelligent Systems. doi: 10.1109/gcis.2009.464
  • Donkeys: Dehghani M, Mardaneh M, Malik OP, NouraeiPour SM (2019). “DTO: Donkey Theorem Optimization.” In 2019 27th Iranian Conference on Electrical Engineering (ICEE). doi: 10.1109/iraniancee.2019.8786601
  • Dragonflies: Mirjalili S (2015). “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems.” Neural Computing and Applications, 27(4), 1053-1073. doi: 10.1007/s00521-015-1920-1
  • Duelists: Biyanto TR, Fibrianto HY, Nugroho G, Hatta AM, Listijorini E, Budiati T, Huda H (2016). “Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel.” In Tan Y, Shi Y, Niu B (eds.), Advances in Swarm Intelligence, 39-47. ISBN 978-3-319-41000-5.

E

  • Eagles: Yang X, Deb S (2010). “Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization.” In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 101-111. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-12538-6_9
  • Earthworms: Wang G, Deb S, Coelho LDS (2015). “Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems.” International Journal of Bio-Inspired Computation, 7, 1-23.
  • Ecogeography: Zheng Y, Ling H, Xue J (2014). “Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations.” Computers & Operations Research, 50, 115-127. doi: 10.1016/j.cor.2014.04.013
  • Ecology: Parpinelli RS, Lopes HS (2011). “An eco-inspired evolutionary algorithm applied to numerical optimization.” In 2011 Third World Congress on Nature and Biologically Inspired Computing. doi: 10.1109/nabic.2011.6089631
  • Electromagnetism: Cuevas E, Oliva D, Zaldivar D, Pérez-Cisneros M, Sossa H (2012). “Circle detection using electro-magnetism optimization.” Information Sciences, 182(1), 40-55. doi: 10.1016/j.ins.2010.12.024
  • Electrons: Discharge: Rahmanzadeh S, Pishvaee MS (2019). “Electron radar search algorithm: a novel developed meta-heuristic algorithm.” Soft Computing, 24(11), 8443-8465. doi: 10.1007/s00500-019-04410-8
  • Electrons: Flow: Khalafallah A, Abdel-Raheem M (2011). “Electimize: New Evolutionary Algorithm for Optimization with Application in Construction Engineering.” Journal of Computing in Civil Engineering, 25(3), 192-201. doi: 10.1061/(asce)cp.1943-5487.0000080
  • Electrons: Radar: Rahmanzadeh S, Pishvaee MS (2019). “Electron radar search algorithm: a novel developed meta-heuristic algorithm.” Soft Computing, 24(11), 8443-8465. doi: 10.1007/s00500-019-04410-8
  • Elephants: Elephant Herds: Wang G, Deb S, dos S. Coelho L (2015). “Elephant Herding Optimization.” In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI). doi: 10.1109/iscbi.2015.8
  • Elephants: Regular Elephants: Deb S, Fong S, Tian Z (2015). “Elephant Search Algorithm for optimization problems.” In 2015 Tenth International Conference on Digital Information Management (ICDIM). doi: 10.1109/icdim.2015.7381893
  • Emotions: Xu Y, Cui Z, Zeng J (2010). “Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems.” In Swarm, Evolutionary, and Memetic Computing, 583-590. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-17563-3_68
  • Epidemics: Huang G (2016). “Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization~algorithm.” Swarm and Evolutionary Computation, 27, 31-67. doi: 10.1016/j.swevo.2015.09.007
  • Experts: Melo VVD (2014). “Kaizen programming.” In Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO \textquotesingle14. doi: 10.1145/2576768.2598264

F

  • Falcons: de Vasconcelos Segundo EH, Mariani VC, dos Santos Coelho L (2019). “Design of heat exchangers using Falcon Optimization Algorithm.” Applied Thermal Engineering, 156, 119-144. doi: 10.1016/j.applthermaleng.2019.04.038
  • Farmland Fertility: Shayanfar H, Gharehchopogh FS (2018). “Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems.” Applied Soft Computing, 71, 728-746. doi: 10.1016/j.asoc.2018.07.033
  • FIFA World Cup: Razmjooy N, Khalilpour M, Ramezani M (2016). “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System.” Journal of Control, Automation and Electrical Systems, 27(4), 419-440. doi: 10.1007/s40313-016-0242-6
  • Fireflies: Yang X (2009). “Firefly Algorithms for Multimodal Optimization.” In Stochastic Algorithms: Foundations and Applications, 169-178. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-04944-6_14
  • Fireworks: Tan Y, Zhu Y (2010). “Fireworks Algorithm for Optimization.” In Lecture Notes in Computer Science, 355-364. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-13495-1_44
  • Fish: Catfish: Chuang L, Tsai S, Yang C (2011). “Improved binary particle swarm optimization using catfish effect for feature selection.” Expert Systems with Applications, 38(10), 12699-12707. doi: 10.1016/j.eswa.2011.04.057
  • Fish: Cuttlefish: Eesa A, Abdulazeez A, Orman Z (2013). “Cuttlefish Algorithm - A Novel Bio-Inspired Optimization Algorithm.” International Journal of Scientific and Engineering Research, 4(9), 1978-1986.
  • Fish: Fish Schools: Filho CJAB, de Lima Neto FB, Lins AJCC, Nascimento AIS, Lima MP (2008). “A novel search algorithm based on fish school behavior.” In 2008 IEEE International Conference on Systems, Man and Cybernetics. doi: 10.1109/icsmc.2008.4811695
  • Fish: Fish Swarms: Li X, Qian J (2003). “Studies on Artificial Fish Swarm Optimization Algorithm Based on Decomposition and Coordination Techniques.” J Circuits Systems, 1, 1-6.
  • Fish: Mouth Brooding: Jahani E, Chizari M (2018). “Tackling global optimization problems with a novel algorithm \textendash Mouth Brooding Fish algorithm.” Applied Soft Computing, 62, 987-1002. doi: 10.1016/j.asoc.2017.09.035
  • Flower Pollination: Yang X (2012). “Flower Pollination Algorithm for Global Optimization.” In Unconventional Computation and Natural Computation, 240-249. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-32894-7_27
  • Forests: Forest Regeneration: Moez H, Kaveh A, Taghizadieh N (2016). “Natural Forest Regeneration Algorithm: A New Meta-Heuristic.” Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(4), 311-326. doi: 10.1007/s40996-016-0042-z
  • Forests: Tree Survival: Ghaemi M, Feizi-Derakhshi M (2014). “Forest Optimization Algorithm.” Expert Systems with Applications, 41(15), 6676-6687. doi: 10.1016/j.eswa.2014.05.009
  • Fox: Red Fox: Połap D, Woźniak M (2021). “Red fox optimization algorithm.” Expert Systems with Applications, 166, 114107. doi: 10.1016/j.eswa.2020.114107
  • Fractals: Salimi H (2015). “Stochastic Fractal Search: A powerful metaheuristic algorithm.” Knowledge-Based Systems, 75, 1-18. doi: 10.1016/j.knosys.2014.07.025
  • Frogs: Japanese Tree Frogs: Hernández H, Blum C (2012). “Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs.” Swarm Intelligence, 6(2), 117-150. doi: 10.1007/s11721-012-0067-2
  • Frogs: Leaping: Eusuff MM, Lansey KE (2003). “Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm.” Journal of Water Resources Planning and Management, 129(3), 210-225. doi: 10.1061/(asce)0733-9496(2003)129:3(210)
  • Fruit Fly: Pan W (2012). “A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example.” Knowledge-Based Systems, 26, 69-74. doi: 10.1016/j.knosys.2011.07.001

G

  • Galaxies: Hosseini HS (2011). “Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation.” International Journal of Computational Science and Engineering, 6(1/2), 132. doi: 10.1504/ijcse.2011.041221
  • Galaxies: Motion: Muthiah-Nakarajan V, Noel MM (2016). “Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion.” Applied Soft Computing, 38, 771-787. doi: 10.1016/j.asoc.2015.10.034
  • Gas Molecules: Brownian Motion: Abdechiri M, Meybodi MR, Bahrami H (2013). “Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO).” Applied Soft Computing, 13(5), 2932-2946. doi: 10.1016/j.asoc.2012.03.068
  • Gas Molecules: Kinetic Energy: Moein S, Logeswaran R (2014). “KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules.” Information Sciences, 275, 127-144. doi: 10.1016/j.ins.2014.02.026
  • Gene Expression: Ferreira C (2002). “Gene Expression Programming in Problem Solving.” In Soft Computing and Industry, 635-653. Springer London. doi: 10.1007/978-1-4471-0123-9_54
  • General Relativity: Beiranvand H, Rokrok E (2015). “General Relativity Search Algorithm: A Global Optimization Approach.” International Journal of Computational Intelligence and Applications, 14(03), 1550017. doi: 10.1142/s1469026815500170
  • Genes: Holland J (1975). Adaptation in Natural and Artificial Systems, An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press.
  • Glow Worms: Krishnanand KN, Ghose D (2008). “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions.” Swarm Intelligence, 3(2), 87-124. doi: 10.1007/s11721-008-0021-5
  • Grasshoppers: Saremi S, Mirjalili S, Lewis A (2017). “Grasshopper Optimisation Algorithm: Theory and application.” Advances in Engineering Software, 105, 30-47. doi: 10.1016/j.advengsoft.2017.01.004
  • Gravitation: Rashedi E, Nezamabadi-pour H, Saryazdi S (2009). “GSA: A Gravitational Search Algorithm.” Information Sciences, 179(13), 2232-2248. doi: 10.1016/j.ins.2009.03.004
  • Gravitation: Interactions: Flores JJ, López R, Barrera J (2011). “Gravitational Interactions Optimization.” In Lecture Notes in Computer Science, 226-237. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-25566-3_17
  • Great Deluge: Dueck G (1993). “New Optimization Heuristics: The Great Deluge and Record to Record Travel.” Journal of Computational Physics, 104(1), 86-92. doi: 10.1006/jcph.1993.1010
  • Grenades: Ahrari A, Atai AA (2010). “Grenade Explosion Method—A novel tool for optimization of multimodal functions.” Applied Soft Computing, 10(4), 1132-1140. doi: 10.1016/j.asoc.2009.11.032
  • Group Counselling: Eita MA, Fahmy MM (2009). “Group Counseling Optimization: A Novel Approach.” In Research and Development in Intelligent Systems XXVI, 195-208. Springer London. doi: 10.1007/978-1-84882-983-1_14
  • Group Decision-Making: Zhang Q, Wang R, Yang J, Ding K, Li Y, Hu J (2017). “Collective decision optimization algorithm: A new heuristic optimization method.” Neurocomputing, 221, 123-137. doi: 10.1016/j.neucom.2016.09.068

H

  • Hawks: Harris's Hawk: DeBruyne AS, Kaur BD (2016). “Harris's Hawk Multi-Objective Optimizer for Reference Point Problems.” In Proceedings on the International Conference on Artificial Intelligence (ICAI), 287-292.
  • Heart: Hatamlou A (2014). “Heart: a novel optimization algorithm for cluster analysis.” Progress in Artificial Intelligence, 2(2-3), 167-173. doi: 10.1007/s13748-014-0046-5
  • Herds: Selfish: Fausto F, Cuevas E, Valdivia A, González A (2017). “A global optimization algorithm inspired in the behavior of selfish herds.” Biosystems, 160, 39-55. doi: 10.1016/j.biosystems.2017.07.010
  • Hoopoe: El-Dosuky M, El-Bassiouny A, Hamza T, Rashad M (2012). “New Hoopoe Heuristic Optimization.” International Journal of Science and Advanced Technology, 2(9), 85-90.
  • Hormones: Zheng K, Tang D, Giret A, Salido MA, Sang Z (2016). “A hormone regulation\textendashbased approach for distributed and on-line scheduling of machines and automated guided vehicles.” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(1), 99-113. doi: 10.1177/0954405416662078
  • Horses: Moldovan D (2020). “Horse Optimization Algorithm: A Novel Bio-Inspired Algorithm for Solving Global Optimization Problems.” In Advances in Intelligent Systems and Computing, 195-209. Springer International Publishing. doi: 10.1007/978-3-030-51971-1_16
  • Humans: Hunting: Brammya G, Praveena S, Preetha NSN, Ramya R, Rajakumar BR, Binu D (2019). “Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm.” The Computer Journal. doi: 10.1093/comjnl/bxy133
  • Humans: Life Choices: Khatri A, Gaba A, Rana KPS, Kumar V (2019). “A novel life choice-based optimizer.” Soft Computing, 24(12), 9121-9141. doi: 10.1007/s00500-019-04443-z
  • Humans: Search and Rescue: Shabani A, Asgarian B, Gharebaghi SA, Salido MA, Giret A (2019). “A New Optimization Algorithm Based on Search and Rescue Operations.” Mathematical Problems in Engineering, 2019, 1-23. doi: 10.1155/2019/2482543
  • Humans: Students: Das B, Mukherjee V, Das D (2020). “Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems.” Advances in Engineering Software, 146, 102804. doi: 10.1016/j.advengsoft.2020.102804
  • Hyenas: Dhiman G, Kumar V (2017). “Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications.” Advances in Engineering Software, 114, 48-70. doi: 10.1016/j.advengsoft.2017.05.014

I

  • Interior Design: Gandomi AH (2014). “Interior search algorithm (ISA): A novel approach for global optimization.” ISA Transactions, 53(4), 1168-1183. doi: 10.1016/j.isatra.2014.03.018
  • Invasive Weeds: Mehrabian A, Lucas C (2006). “A novel numerical optimization algorithm inspired from weed colonization.” Ecological Informatics, 1(4), 355-366. doi: 10.1016/j.ecoinf.2006.07.003
  • Ions: Javidy B, Hatamlou A, Mirjalili S (2015). “Ions motion algorithm for solving optimization problems.” Applied Soft Computing, 32, 72-79. doi: 10.1016/j.asoc.2015.03.035

J

  • Jaguars: Chen C, Tsai Y, Liu I, Lai C, Yeh Y, Kuo S, Chou Y (2015). “A Novel Metaheuristic: Jaguar Algorithm with Learning Behavior.” In 2015 IEEE International Conference on Systems, Man, and Cybernetics. doi: 10.1109/smc.2015.282

K

  • Keshtel Duck: Hajiaghaei-Keshteli M, Aminnayeri M (2014). “Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm.” Applied Soft Computing, 25, 184-203. doi: 10.1016/j.asoc.2014.09.034
  • Kestrels: Agbehadji IE, Millham R, Fong S (2016). “Kestrel-Based Search Algorithm for Association Rule Mining and Classification of Frequently Changed Items.” In 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN). doi: 10.1109/cicn.2016.76
  • Kidneys: Jaddi NS, Alvankarian J, Abdullah S (2017). “Kidney-inspired algorithm for optimization problems.” Communications in Nonlinear Science and Numerical Simulation, 42, 358-369. doi: 10.1016/j.cnsns.2016.06.006
  • Krill: Gandomi AH, Alavi AH (2012). “Krill herd: A new bio-inspired optimization algorithm.” Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845. doi: 10.1016/j.cnsns.2012.05.010

L

  • Ladybirds: Wang P, Zhu Z, Huang S (2013). “Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization.” The Scientific World Journal, 2013, 1-11. doi: 10.1155/2013/378515
  • Lightning: Shareef H, Ibrahim AA, Mutlag AH (2015). “Lightning search algorithm.” Applied Soft Computing, 36, 315-333. doi: 10.1016/j.asoc.2015.07.028
  • Lions: Wang B, Jin X, Cheng B (2012). “Lion pride optimizer: An optimization algorithm inspired by lion pride behavior.” Science China Information Sciences, 55(10), 2369-2389. doi: 10.1007/s11432-012-4548-0
  • Locusts: Chen S (2009). “An Analysis of Locust Swarms on Large Scale Global Optimization Problems.” In Artificial Life: Borrowing from Biology, 211-220. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-10427-5_21

M

  • Marine Predators: Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020). “Marine Predators Algorithm: A nature-inspired metaheuristic.” Expert Systems with Applications, 152, 113377. doi: 10.1016/j.eswa.2020.113377
  • Markets: Ghorbani N, Babaei E (2014). “Exchange market algorithm.” Applied Soft Computing, 19, 177-187. doi: 10.1016/j.asoc.2014.02.006
  • Mayflies: Zervoudakis K, Tsafarakis S (2020). “A mayfly optimization algorithm.” Computers & Industrial Engineering, 145, 106559. doi: 10.1016/j.cie.2020.106559
  • Meerkats: Klein CE, dos Santos Coelho L (2018). “Meerkats-inspired Algorithm for Global Optimization Problems.” In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
  • Mice: Wild Mice: Nejatian S, Omidvar R, Parvin H, Rezaei V, Yasrebi M (2019). “A New Algorithm: Wild Mice Colony Algorithm (WMC).” TABRIZ JOURNAL OF ELECTRICAL ENGINEERING.
  • Mine Explosions: Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2012). “Mine blast algorithm for optimization of truss structures with discrete variables.” Computers & Structures, 102-103, 49-63. doi: 10.1016/j.compstruc.2012.03.013
  • Molecular Dynamics: Zhao W, Wang L, Zhang Z (2019). “Atom search optimization and its application to solve a hydrogeologic parameter estimation problem.” Knowledge-Based Systems, 163, 283-304. doi: 10.1016/j.knosys.2018.08.030
  • Monkeys: Monkey Foraging: Mucherino A, Seref O, Seref O, Kundakcioglu OE, Pardalos P (2007). “Monkey search: a novel metaheuristic search for global optimization.” In AIP Conference Proceedings. doi: 10.1063/1.2817338
  • Monkeys: Spider Monkeys: Bansal JC, Sharma H, Jadon SS, Clerc M (2014). “Spider Monkey Optimization algorithm for numerical optimization.” Memetic Computing, 6(1), 31-47. doi: 10.1007/s12293-013-0128-0
  • Mosquitos: Egg-laying Behavior: ul Amir Afsar Minhas F, Arif M (2011). “MOX: A novel global optimization algorithm inspired from Oviposition site selection and egg hatching inhibition in mosquitoes.” Applied Soft Computing, 11(8), 4614-4625. doi: 10.1016/j.asoc.2011.07.020
  • Mosquitos: Flying Behavior: Alauddin M (2016). “Mosquito flying optimization (MFO).” In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). doi: 10.1109/iceeot.2016.7754783
  • Moths: Mirjalili S (2015). “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm.” Knowledge-Based Systems, 89, 228-249. doi: 10.1016/j.knosys.2015.07.006
  • Mountain Climbers: Zhang LM, Dahlmann C, Zhang Y (2009). “Human-Inspired Algorithms for continuous function optimization.” In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. doi: 10.1109/icicisys.2009.5357838
  • Multiverse: Mirjalili S, Mirjalili SM, Hatamlou A (2015). “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization.” Neural Computing and Applications, 27(2), 495-513. doi: 10.1007/s00521-015-1870-7
  • Mushroom Reproduction: Bidar M, Kanan HR, Mouhoub M, Sadaoui S (2018). “Mushroom Reproduction Optimization (MRO): A Novel Nature-Inspired Evolutionary Algorithm.” In 2018 IEEE Congress on Evolutionary Computation.
  • Musicians: Geem ZW, Kim JH, Loganathan G (2001). “A New Heuristic Optimization Algorithm: Harmony Search.” SIMULATION, 76(2), 60-68. doi: 10.1177/003754970107600201

N

  • Neurons: Asil Gharebaghi S, Ardalan Asl M (2017). “NEW META-HEURISTIC OPTIMIZATION ALGORITHM USING NEURONAL COMMUNICATION.” _ International Journal of Optimization in Civil Engineering_, 7(3). http://ijoce.iust.ac.ir/article-1-306-en.pdf, <URL: http://ijoce.iust.ac.ir/article-1-306-en.html>.
  • Newton's Cooling Law: Kaveh A, Dadras A (2017). “A novel meta-heuristic optimization algorithm: Thermal exchange optimization.” Advances in Engineering Software, 110, 69-84. doi: 10.1016/j.advengsoft.2017.03.014
  • Nuclear Collision: Sacco WF, Oliveira C (2005). “A new stochastic optimization algorithm based on a particle collision metaheuristic.” Proceedings of 6th WCSMO.

O

  • Optics: Kashan AH (2015). “A new metaheuristic for optimization: Optics inspired optimization (OIO).” Computers & Operations Research, 55, 99-125. doi: 10.1016/j.cor.2014.10.011
  • Owls: de Vasconcelos Segundo EH, Mariani VC, dos Santos Coelho L (2019). “Metaheuristic inspired on owls behavior applied to heat exchangers design.” Thermal Science and Engineering Progress, 14, 100431. doi: 10.1016/j.tsep.2019.100431

P

  • Paddy Fields: Premaratne U, Samarabandu J, Sidhu T (2009). “A new biologically inspired optimization algorithm.” In 2009 International Conference on Industrial and Information Systems (ICIIS). doi: 10.1109/iciinfs.2009.5429852
  • Penguins: Gheraibia Y, Moussaoui A (2013). “Penguins Search Optimization Algorithm (PeSOA).” In Recent Trends in Applied Artificial Intelligence, 222-231. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-38577-3_23
  • Penguins: Emperor Penguins: Dhiman G, Kumar V (2018). “Emperor penguin optimizer: A bio-inspired algorithm for engineering problems.” Knowledge-Based Systems, 159, 20-50. doi: 10.1016/j.knosys.2018.06.001
  • Peral Hunting: Chan CY, Xue F, Ip WH, Cheung CF (2012). “A Hyper-Heuristic Inspired by Pearl Hunting.” In Lecture Notes in Computer Science, 349-353. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-34413-8_26
  • Pigeons: Duan H, Qiao P (2014). “Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning.” International Journal of Intelligent Computing and Cybernetics, 7(1), 24-37.
  • Pigeons: Feeding: Lamy J (2018). “Artificial Feeding Birds (AFB): A New Metaheuristic Inspired by the Behavior of Pigeons.” In Advances in Nature-Inspired Computing and Applications, 43-60. Springer International Publishing. doi: 10.1007/978-3-319-96451-5_3
  • Plants: Plant Growth: Li J, Cui Z, Shi Z (2012). “An Improved Artificial Plant Optimization Algorithm for Coverage Problem in WSN.” Sensor Letters, 10(8), 1874-1878. doi: 10.1166/sl.2012.2627
  • Plants: Plant Intelligence: Akyol S, Alatas B (2016). “Plant intelligence based metaheuristic optimization algorithms.” Artificial Intelligence Review, 47(4), 417-462. doi: 10.1007/s10462-016-9486-6
  • Plants: Plant Propagation: Sulaiman M, Salhi A, Selamoglu BI, Kirikchi OB (2014). “A Plant Propagation Algorithm for Constrained Engineering Optimisation Problems.” Mathematical Problems in Engineering, 2014, 1-10. doi: 10.1155/2014/627416
  • Plants: Sapling Growth: Karci A, Alatas B (2006). “Thinking capability of saplings growing up algorithm.” In International Conference on Intelligent Data Engineering and Automated Learning, 386-393. Springer.
  • Polar Bears: Połap D, Woz\textasciiacuteniak M (2017). “Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism.” Symmetry, 9(10), 203. doi: 10.3390/sym9100203
  • Politics: Imperialism: Atashpaz-Gargari E, Lucas C (2007). “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition.” In 2007 IEEE Congress on Evolutionary Computation. doi: 10.1109/cec.2007.4425083
  • Politics: Parliamentarist Elections: Borji A (2007). “A New Global Optimization Algorithm Inspired by Parliamentary Political Competitions.” In MICAI 2007: Advances in Artificial Intelligence, 61-71. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-76631-5_7
  • Politics: Presidential Elections: Emami H, Derakhshan F (2015). “Election algorithm: A new socio-politically inspired strategy.” AI Communications, 28(3), 591–603. ISSN 18758452, 09217126, doi: 10.3233/AIC-140652
  • Politics: Strategies: Melvix JL (2014). “Greedy Politics Optimization: Metaheuristic inspired by political strategies adopted during state assembly elections.” In 2014 IEEE International Advance Computing Conference (IACC). doi: 10.1109/iadcc.2014.6779490

Q

  • Quantum Superposition: Saire JEC, Tupac VYJ (2015). “An approach to real-coded quantum inspired evolutionary algorithm using particles filter.” In 2015 Latin America Congress on Computational Intelligence (LA-CCI). doi: 10.1109/la-cci.2015.7435984

R

  • Ravens: Torabi S, Safi-Esfahani F (2017). “Improved Raven Roosting Optimization algorithm (IRRO).” Swarm and Evolutionary Computation. doi: 10.1016/j.swevo.2017.11.006
  • Rays of Light: Kaveh A, Khayatazad M (2012). “A new meta-heuristic method: Ray Optimization.” Computers & Structures, 112-113, 283-294. doi: 10.1016/j.compstruc.2012.09.003
  • Reincarnation: Sharma A (2010). “A new optimizing algorithm using reincarnation concept.” In 2010 11th International Symposium on Computational Intelligence and Informatics (CINTI). doi: 10.1109/cinti.2010.5672231
  • Rhinoceros: Wang G, Gao X, Zenger K, Coelho LdS (2016). “A novel metaheuristic algorithm inspired by rhino herd behavior.” In Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016, number 142, 1026-1033. Linköping University Electronic Press.
  • Rice: Hybrid: Ye Z, Ma L, Chen H (2016). “A hybrid rice optimization algorithm.” In 2016 11th International Conference on Computer Science & Education (ICCSE). doi: 10.1109/iccse.2016.7581575
  • River Formation: Rabanal P, Rodr'\iguez I, Rubio F (2007). “Using River Formation Dynamics to Design Heuristic Algorithms.” In Lecture Notes in Computer Science, 163-177. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-73554-0_16
  • Roach Infestations: Havens TC, Spain CJ, Salmon NG, Keller JM (2008). “Roach Infestation Optimization.” In 2008 IEEE Swarm Intelligence Symposium. doi: 10.1109/sis.2008.4668317
  • Roots: Merrikh-Bayat F (2015). “The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature.” Applied Soft Computing, 33, 292-303. doi: 10.1016/j.asoc.2015.04.048

S

  • Sailfish: Shadravan S, Naji H, Bardsiri V (2019). “The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems.” Engineering Applications of Artificial Intelligence, 80, 20-34. doi: 10.1016/j.engappai.2019.01.001
  • Salmon Migrations: Mozaffari A, Fathi A, Behzadipour S (2012). “The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation.” International Journal of Bio-Inspired Computation, 4(5), 286-301.
  • Salp Planktons: Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017). “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems.” Advances in Engineering Software, 114, 163-191. doi: 10.1016/j.advengsoft.2017.07.002
  • Sandpiper: Kaur A, Jain S, Goel S (2019). “Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems.” Applied Intelligence, 50(2), 582-619. doi: 10.1007/s10489-019-01507-3
  • Scientific Method: Felipe D, Goldbarg EFG, Goldbarg MC (2014). “Scientific algorithms for the Car Renter Salesman Problem.” In 2014 IEEE Congress on Evolutionary Computation (CEC). doi: 10.1109/cec.2014.6900556
  • Seagulls: Dhiman G, Kumar V (2019). “Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems.” Knowledge-Based Systems, 165, 169-196. doi: 10.1016/j.knosys.2018.11.024
  • See-See Partridges: Omidvar R, Parvin H, Rad F (2015). “SSPCO Optimization Algorithm (See-See Partridge Chicks Optimization).” In 2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI). doi: 10.1109/micai.2015.22
  • Sharks: Abedinia O, Amjady N, Ghasemi A (2014). “A new metaheuristic algorithm based on shark smell optimization.” Complexity, 21(5), 97-116. doi: 10.1002/cplx.21634
  • Sharks: Hammerhead: Ali A, Zafar K, Bakhshi T (2019). “On Nature-Inspired Dynamic Route Planning: Hammerhead Shark Optimization Algorithm.” In 2019 15th International Conference on Emerging Technologies (ICET). doi: 10.1109/icet48972.2019.8994757
  • Sheep Flocks: Kim H, Ahn B (2001). “A new evolutionary algorithm based on sheep flocks heredity model.” In 2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233). doi: 10.1109/pacrim.2001.953683
  • Sine Waves: Tanyildizi E, Demir G (2017). “Golden sine algorithm: a novel math-inspired algorithm.” Advances in Electrical and Computer Engineering, 17(2), 71-79.
  • Slime Mold: Monismith DR, Mayfield BE (2008). “Slime Mold as a model for numerical optimization.” In 2008 IEEE Swarm Intelligence Symposium. doi: 10.1109/sis.2008.4668295
  • Small World: Du H, Wu X, Zhuang J (2006). “Small-World Optimization Algorithm for Function Optimization.” In Lecture Notes in Computer Science, 264-273. Springer Berlin Heidelberg. doi: 10.1007/11881223_33
  • Soccer: League: Moosavian N, Roodsari BK (2014). “Soccer League Competition Algorithm, a New Method for Solving Systems of Nonlinear Equations.” International Journal of Intelligence Science, 04(01), 7-16. doi: 10.4236/ijis.2014.41002
  • Soccer: Soccer Games: Purnomo HD, Wee H (2013). “Soccer Game Optimization.” In Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, 386-420. IGI Global. doi: 10.4018/978-1-4666-2086-5.ch013
  • Soccer: Soccer Tournaments: Osaba E, Diaz F, Onieva E (2014). “Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts.” Applied Intelligence, 41(1), 145-166. doi: 10.1007/s10489-013-0512-y
  • Soccer: Style: Rashid MFFA (2020). “Tiki-taka algorithm: a novel metaheuristic inspired by football playing style.” Engineering Computations, 38(1), 313-343. doi: 10.1108/ec-03-2020-0137
  • Social Behavior: Ray T, Liew K (2003). “Society and civilization: an optimization algorithm based on the simulation of social behavior.” IEEE Transactions on Evolutionary Computation, 7(4), 386-396. doi: 10.1109/tevc.2003.814902
  • Social Behavior: Poor and Rich: Moosavi SHS, Bardsiri VK (2019). “Poor and rich optimization algorithm: A new human-based and multi populations algorithm.” Engineering Applications of Artificial Intelligence, 86, 165-181. doi: 10.1016/j.engappai.2019.08.025
  • Social Behavior: Queuing: Zhang J, Xiao M, Gao L, Pan Q (2018). “Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems.” Applied Mathematical Modelling, 63, 464-490.
  • Social Behavior: Seeking: Dai C, Chen W, Zhu Y (2010). “Seeker Optimization Algorithm for Digital IIR Filter Design.” IEEE Transactions on Industrial Electronics, 57(5), 1710-1718. doi: 10.1109/tie.2009.2031194
  • Social Behavior: Thieves and Police: Bagheri H, Ara AL, Hosseini R (2019). “Thieves and Police, a New Optimization Algorithm: Theory and Application in Probabilistic Power Flow.” IETE Journal of Research, 1-18. doi: 10.1080/03772063.2019.1672586
  • Social Behavior: Urbanization: Ghasemian H, Ghasemian F, Vahdat-Nejad H (2020). “Human urbanization algorithm: A novel metaheuristic approach.” Mathematics and Computers in Simulation, 178, 1-15. doi: 10.1016/j.matcom.2020.05.023
  • Social Engineering: Fard AMF, Hajiaghaei-Keshteli M (2017). “Social Engineering Optimization (SEO); A New Single-Solution Meta-heuristic Inspired by Social Engineering.” In International Conference on Industrial Engineering.
  • Social Spiders: Cuevas E, Cienfuegos M, Zald'\ivar D, Pérez-Cisneros M (2013). “A swarm optimization algorithm inspired in the behavior of the social-spider.” Expert Systems with Applications, 40(16), 6374-6384. doi: 10.1016/j.eswa.2013.05.041
  • Sonar: Tzanetos A, Dounias G (2017). “A New Metaheuristic Method for Optimization: Sonar Inspired Optimization.” In Boracchi G, Iliadis L, Jayne C, Likas A (eds.), Engineering Applications of Neural Networks, 417-428. ISBN 978-3-319-65172-9.
  • Sooty Tern: Dhiman G, Kaur A (2019). “STOA: A bio-inspired based optimization algorithm for industrial engineering problems.” Engineering Applications of Artificial Intelligence, 82, 148-174. doi: 10.1016/j.engappai.2019.03.021
  • Sperm: Raouf OA, Hezam IM (2017). “Sperm motility algorithm: a novel metaheuristic approach for global optimisation.” International Journal of Operational Research, 28(2), 143. doi: 10.1504/ijor.2017.10002079
  • Spirals: Tamura K, and Keiichiro Yasuda (2011). “Spiral Dynamics Inspired Optimization.” Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(8), 1116-1122. doi: 10.20965/jaciii.2011.p1116
  • Sports Championships: Kashan AH (2009). “League Championship Algorithm: A New Algorithm for Numerical Function Optimization.” In 2009 International Conference of Soft Computing and Pattern Recognition. doi: 10.1109/socpar.2009.21
  • Squirrels: Flying Squirrels: Jain M, Singh V, Rani A (2018). “A novel nature-inspired algorithm for optimization: Squirrel search algorithm.” Swarm and Evolutionary Computation. doi: 10.1016/j.swevo.2018.02.013
  • States of Matter: Cuevas E, Reyna-Orta A, D'\iaz-Cortes M (2017). “A Multimodal Optimization Algorithm Inspired by the States of Matter.” Neural Processing Letters, 48(1), 517-556. doi: 10.1007/s11063-017-9750-z
  • Swallows: Neshat M, Sepidnam G, Sargolzaei M (2012). “Swallow swarm optimization algorithm: a new method to optimization.” Neural Computing and Applications, 23(2), 429-454. doi: 10.1007/s00521-012-0939-9
  • Symbiotic Organisms: Cheng M, Prayogo D (2014). “Symbiotic Organisms Search: A new metaheuristic optimization algorithm.” Computers & Structures, 139, 98-112. doi: 10.1016/j.compstruc.2014.03.007

T

  • Teachers: Rao R, Savsani V, Vakharia D (2011). “Teaching\textendashlearning-based optimization: A novel method for constrained mechanical design optimization problems.” Computer-Aided Design, 43(3), 303-315. doi: 10.1016/j.cad.2010.12.015
  • Termites: Hedayatzadeh R, Salmassi FA, Keshtgari M, Akbari R, Ziarati K (2010). “Termite colony optimization: A novel approach for optimizing continuous problems.” In 2010 18th Iranian Conference on Electrical Engineering. doi: 10.1109/iraniancee.2010.5507009
  • Troops of Soldiers: Chen T (2009). “A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searching Swarm Algorithm and Its Performance Analysis.” In 2009 International Joint Conference on Computational Sciences and Optimization. doi: 10.1109/cso.2009.183
  • Tug of War: Kaveh A, Zolghadr A (2016). “A novel meta-heuristic algorithm: tug of war optimization.” Iran University of Science & Technology, 6(4), 469-492.
  • Turnicates: Kaur S, Awasthi LK, Sangal A, Dhiman G (2020). “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization.” Engineering Applications of Artificial Intelligence, 90, 103541. doi: 10.1016/j.engappai.2020.103541

U

V

  • Vaccination: Tayeb FB, Bessedik M, Benbouzid M, Cheurfi H, Blizak A (2017). “Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring.” Procedia Computer Science, 112, 427-436. doi: 10.1016/j.procs.2017.08.055
  • Vehicles: Savsani P, Savsani V (2016). “Passing vehicle search (PVS): A novel metaheuristic algorithm.” Applied Mathematical Modelling, 40(5-6), 3951-3978. doi: 10.1016/j.apm.2015.10.040
  • Vibrating Particles: Kaveh A, Ghazaan MI (2016). “Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints.” Acta Mechanica, 228(1), 307-322. doi: 10.1007/s00707-016-1725-z
  • Virus: Attacking: Liang Y, Juarez JRC (2015). “A novel metaheuristic for continuous optimization problems: Virus optimization algorithm.” Engineering Optimization, 48(1), 73-93. doi: 10.1080/0305215x.2014.994868
  • Virus: Swine Flu: Pattnaik S, Bakwad K, Sohi B, Ratho R, Devi S (2013). “Swine Influenza Models Based Optimization (SIMBO).” Applied Soft Computing, 13(1), 628-653. doi: 10.1016/j.asoc.2012.07.010
  • Viruses: Virulence: Jaderyan M, Khotanlou H (2016). “Virulence Optimization Algorithm.” Applied Soft Computing, 43, 596-618. doi: 10.1016/j.asoc.2016.02.038
  • Viruses: Virus Colonies: Li MD, Zhao H, Weng XW, Han T (2016). “A novel nature-inspired algorithm for optimization: Virus colony search.” Advances in Engineering Software, 92, 65-88. doi: 10.1016/j.advengsoft.2015.11.004
  • Viruses: Virus Replication: Cortés P, Garc'\ia JM, Muñuzuri J, Onieva L (2008). “Viral systems: A new bio-inspired optimisation approach.” Computers & Operations Research, 35(9), 2840-2860. doi: 10.1016/j.cor.2006.12.018
  • Volleyball Leagues: Moghdani R, Salimifard K (2018). “Volleyball Premier League Algorithm.” Applied Soft Computing, 64, 161-185. doi: 10.1016/j.asoc.2017.11.043
  • Vortices: Doğan B, Ölmez T (2015). “A new metaheuristic for numerical function optimization: Vortex Search algorithm.” Information Sciences, 293, 125-145. doi: 10.1016/j.ins.2014.08.053
  • Vultures: Sur C, Sharma S, Shukla A (2013). “Egyptian Vulture Optimization Algorithm \textendash A New Nature Inspired Meta-heuristics for Knapsack Problem.” In The 9th International Conference on Computing and InformationTechnology (IC2IT2013), 227-237. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-37371-8_26

W

  • Wasps: Pinto P, Runkler TA, Sousa JM (2005). “Wasp swarm optimization of logistic systems.” In Adaptive and Natural Computing Algorithms, 264-267. Springer.
  • Water: Hydrological Cycle: Wedyan A, Whalley J, Narayanan A (2017). “Hydrological Cycle Algorithm for Continuous Optimization Problems.” Journal of Optimization, 2017, 1-25. doi: 10.1155/2017/3828420
  • Water: Intelligent Water Drops: Hosseini HS (2009). “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm.” International Journal of Bio-Inspired Computation, 1(1/2), 71. doi: 10.1504/ijbic.2009.022775
  • Water: Rain: Kaboli SHA, Selvaraj J, Rahim N (2017). “Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems.” Journal of Computational Science, 19, 31-42. doi: 10.1016/j.jocs.2016.12.010
  • Water: Rain Drops: Jiang Q, Wang L, Hei X, Fei R, Yang D, Zou F, Li H, Cao Z, Lin Y (2014). “Optimal approximation of stable linear systems with a novel and efficient optimization algorithm.” In 2014 IEEE Congress on Evolutionary Computation (CEC). doi: 10.1109/cec.2014.6900366
  • Water: Water Cycle: Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012). “Water cycle algorithm \textendash A novel metaheuristic optimization method for solving constrained engineering optimization problems.” Computers & Structures, 110-111, 151-166. doi: 10.1016/j.compstruc.2012.07.010
  • Water: Water Evaporation: Kaveh A, Bakhshpoori T (2016). “Water Evaporation Optimization: A novel physically inspired optimization algorithm.” Computers & Structures, 167, 69-85. doi: 10.1016/j.compstruc.2016.01.008
  • Water: Water Flow: Tran TH, Ng KM (2010). “A water-flow algorithm for flexible flow shop scheduling with~intermediate buffers.” Journal of Scheduling, 14(5), 483-500. doi: 10.1007/s10951-010-0205-x
  • Water: Water Wave: Zheng Y (2015). “Water wave optimization: A new nature-inspired metaheuristic.” Computers & Operations Research, 55, 1-11. doi: 10.1016/j.cor.2014.10.008
  • Whales: Binary Whales: K. SR, Panwar L, Panigrahi BK, Kumar R (2018). “Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets.” Engineering Optimization, 51(3), 369-389. doi: 10.1080/0305215x.2018.1463527
  • Whales: Killer Whales: Biyanto TR, Matradji, Irawan S, Febrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama JA, Bethiana TN (2017). “Killer Whale Algorithm: An Algorithm Inspired by the Life of Killer Whale.” Procedia Computer Science, 124, 151-157. doi: 10.1016/j.procs.2017.12.141
  • Whales: Orcas: Drias H, Drias Y, Khennak I (2020). “A New Swarm Algorithm Based on Orcas Intelligence for Solving Maze Problems.” In Trends and Innovations in Information Systems and Technologies, 788-797. Springer International Publishing. doi: 10.1007/978-3-030-45688-7_77
  • Whales: Regular Whales: Mirjalili S, Lewis A (2016). “The Whale Optimization Algorithm.” Advances in Engineering Software, 95, 51-67. doi: 10.1016/j.advengsoft.2016.01.008
  • Whales: Sperm Whales: Ebrahimi A, Khamehchi E (2016). “Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems.” Journal of Natural Gas Science and Engineering, 29, 211-222. doi: 10.1016/j.jngse.2016.01.001
  • Whirlpools: Ghasemi M, Davoudkhani IF, Akbari E, Rahimnejad A, Ghavidel S, Li L (2020). “A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO).” Engineering Applications of Artificial Intelligence, 92, 103666. doi: 10.1016/j.engappai.2020.103666
  • Wind: Bayraktar Z, Komurcu M, Werner DH (2010). “Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics.” In 2010 IEEE Antennas and Propagation Society International Symposium. doi: 10.1109/aps.2010.5562213
  • Wolves: Grey Wolves: Mirjalili S, Mirjalili SM, Lewis A (2014). “Grey Wolf Optimizer.” Advances in Engineering Software, 69, 46-61. doi: 10.1016/j.advengsoft.2013.12.007
  • Wolves: Wolves: Tang R, Fong S, Yang X, Deb S (2012). “Wolf search algorithm with ephemeral memory.” In Seventh International Conference on Digital Information Management (ICDIM 2012). doi: 10.1109/icdim.2012.6360147
  • Worms: Arnaout J (2014). “Worm optimization: a novel optimization algorithm inspired by C. Elegans.” In Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management, 2499-2505.

X

Y

  • Yellow Saddle Goldfish: Zald'\ivar D, Morales B, Rodr'\iguez A, Valdivia-G A, Cuevas E, Pérez-Cisneros M (2018). “A novel bio-inspired optimization model based on Yellow Saddle Goatfish behavior.” Biosystems, 174, 1-21. doi: 10.1016/j.biosystems.2018.09.007
  • Yin-Yang Pairs: Punnathanam V, Kotecha P (2016). “Yin-Yang-pair Optimization: A novel lightweight optimization algorithm.” Engineering Applications of Artificial Intelligence, 54, 62-79. doi: 10.1016/j.engappai.2016.04.004

Z

  • Zombies: Nguyen HT, Bhanu B (2012). “Zombie Survival Optimization: A swarm intelligence algorithm inspired by zombie foraging.” In Pattern Recognition (ICPR), 2012 21st International Conference on, 987-990. IEEE.

Maintainers

("the Zoo Keepers")

Contributors

(at least one contribution to the bestiary - in terms of adding a method to the list, not inventing it!)

  • Adré Steyn - University of Stellenbosch, South Africa
  • Alberto Franzin - Université Libre de Bruxelles, Belgium
  • Anand Subramanian - UFPB, Brazil
  • André Maravilha - UFMG, Brazil
  • Carlos Fonseca - University of Coimbra, Portugal
  • Ciniro Nametala - UFMG, Brazil
  • Christian L. Camacho Villalón - ULB, Brussels
  • Eduardo Hauck - UFJF, Brazil
  • Fabio Daolio - University of Stirling, Scotland UK
  • Fernanda Takahashi - UFMG, Brazil
  • Fernando Otero - University of Kent, England UK
  • Fillipe Goulart - UFMG, Brazil
  • Federico Pagnozzi - Université Libre de Bruxelles, Belgium
  • Krystian Lapa - Institute of Computational Intelligence, Poland
  • Iago A. de Carvalho - UFMG, Brazil
  • Iztok Fister Jr. - University of Maribor, Slovenia
  • Jakub Grabski - Poznan University of Technology, Poland
  • James Brookhouse - University of Kent, England UK
  • Juan Carlos Chacon-Hurtado - TU Delft, Netherlands
  • Joao Duro - University of Sheffield, England UK
  • Joaquin A. Pacheco - University of Burgos, Spain
  • Kenneth Sörensen - University of Antwerp, Belgium
  • Konstantinos Zervoudakis - Technical University of Crete, Greece
  • Koen van der Blom - Leiden University, Netherlands
  • Lars Magnus Hvattum - Molde University College, Norway
  • Leandro Santos Coelho - UFPR, Brazil
  • Marc Sevaux - Université de Bretagne-Sud, France
  • Marco Mollinetti - University of Tsukuba, Japan
  • Marco Pranzo - Università di Siena, Italy
  • Marcus Ritt - UFRGS, Brazil
  • Michał Okulewicz - Politechnika Warszawska, Poland
  • Nadarajen Veerapen - University of Stirling, Scotland UK
  • Nguyen Tri Hai - Chung-Ang University, South Korea
  • Paul Rubin - Michigan State University, USA
  • Peter Lewis - Aston University, UK
  • Robin Purshouse - University of Sheffield, England UK
  • Rubén Ruiz - Universitat Politècnica de València, Spain
  • Ruud Koot - Universiteit Utrecht, The Netherlands
  • Sara Silva - University of Lisbon
  • Sander - Leiden University, Netherlands
  • senorramirez
  • Sergio A. Rojas - Universidad Distrital de Bogotá, Colombia
  • Silvano Martello - University of Bologna, Italy
  • Stefan Voß - Universität Hamburg, Germany
  • Thomas Jacob Riis Stidsen - Danmarks Tekniske Universitet, Denmark
  • Thomas Stützle - Université Libre de Bruxelles, Belgium
  • Tushar Semwal - IIT Guwahati, India
  • Yuri Lavinas - University of Tsukuba, Brazil

How to Contribute

If you know a paper that should belong to this list, please send an e-mail to either Claus or Felipe, or report an issue on our Github repo. The criteria for inclusion are quite simple:

  1. the work must be in a peer reviewed publication (journal or conference);
  2. the title or abstract must name the algorithm after the natural (or supernatural) metaphor on which it was based;

It is also important to highlight that only the earliest known mention for each metaphor is included.

More Info:

  • If you liked this list, you should read the paper "Metaheuristic: The Metaphor Exposed", by Kenneth Söresen
  • Need inspiration for your next Bioinspired algorithm? Check Marco Scirea and Julian Togelius' Daily Bio-heuristics bot.
  • Some of the algorithms listed here were found in a list compiled by Iztok Fister Jr. et al., which is available here. Iztok also recently published this paper reflecting on the proliferation of metaphors in EC research.
  • A fantastic parody of this whole metaphor craze can be read here. Highly recommended!

License:

This work is licensed under the Creative Commons CC BY-NC-SA 4.0 license (Attribution Non-Commercial Share Alike International License version 4.0): http://creativecommons.org/licenses/by-nc-sa/4.0/

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A bestiary of evolutionary, swarm and other metaphor-based algorithms

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