Drones, like submarines or the well-known flying machines, such as balloons, airplanes, helicopters, quadcopters, can navigate autonomously or remotely. They have sensors, can communicate over vast distances, can use solar power, and - one of the most important features - can be upgraded or improved not only concerning hardware but also by changing their software (the firmware). Therefore, since these machines have software (firmware) to control their behavior, researchers are free to add mechanisms to the algorithm as simple software upgrades.
Drone Squadron Optimization (DSO) is an artifact-inspired technique, as opposed to many nature-inspired algorithms used today. DSO is very flexible because it is not related to natural behaviors or phenomena. Changing its formulation doesn't make it a 'novel nature-inspired algorithm'. Therefore, although you change DSO's components, it is still DSO.
DSO is a novel self-adaptive metaheuristic for global numerical optimization which is updated online (at running time) by a hyper-heuristic. AFAIK, it is the first meta-heuristic that can improve its own code during the optimization process.
DSO has two core parts: the semi-autonomous drones that fly over a landscape to explore, and the command center that processes the retrieved data and updates the drones' firmware whenever necessary.
[Images from the Internet]
The self-adaptive aspect of DSO in this work is the perturbation/movement scheme, which is the procedure used to generate target coordinates. This procedure is evolved by the command center during the global optimization process in order to adapt DSO to the search landscape.
As one may wonder, DSO can automatically generate formulas that are exactly the same of many nature-inspired meta-heuristics. Moreover, it can automatically improve them!
Because of its automatic adaptation, DSO can do the following:
We provide a Matlab (R) toolbox for testing purposes. It has controls for several parameters, while the others must be changed in the source code.
Also, you can zoom and pan the chart using the top left controls.
Octave 3.8orMatlab (R) 2011
AFAIK, the Toolbox works only in Matlab.
Please, check DSO/main.m
Please, check file DSO/InitGPConfig.m
Please, check file DSO/CostFunction.m
Please, check DSO/DSOToolbox.m
This project is licensed under GNU GPL v3.
To cite DSO in publications use:
de Melo, V.V. & Banzhaf, W. Neural Comput & Applic (2017). doi:10.1007/s00521-017-2881-3
https://link.springer.com/article/10.1007/s00521-017-2881-3
A BibTeX entry for LaTeX users is:
@Article{deMelo2017,
author="de Melo, Vin{\'i}cius Veloso
and Banzhaf, Wolfgang",
title="Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization",
journal="Neural Computing and Applications",
year="2017",
issn="1433-3058",
doi="10.1007/s00521-017-2881-3",
url="http://dx.doi.org/10.1007/s00521-017-2881-3"
}
Vinícius Veloso de Melo (Institute of Science and Technology - Federal University of São Paulo)
Contact: dr.vmelo@gmail.com, vinicius.melo@unifesp.br