What Else Is the Evolution of PSO Telling Us?

Dioşan, Laura; Oltean, Mihai

doi:https://doi.org/10.1155/2008/289564

Journal of Artificial Evolution and Applications

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Particle Swarms: The Second Decade

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Research Article | Open Access

Volume 2008 | Article ID 289564 | https://doi.org/10.1155/2008/289564

What Else Is the Evolution of PSO Telling Us?

Laura Dioşan¹and Mihai Oltean¹

Academic Editor: Alex Freitas

Received20 Jul 2007

Revised29 Nov 2007

Accepted29 Nov 2007

Published28 Jan 2008

Abstract

Evolutionary algorithms (EAs) can be used in order to design particle swarm optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of designing PSO algorithms, we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights about the swarm behavior. The rules that have been observed can help us design better PSO algorithms for optimization. We investigate and analyze swarm phenomena by looking into the process of evolving PSO algorithms. Several test problems have been analyzed in the experiments and interesting facts can be inferred from the strategy evolution process (the particle quality could influence the update order, some particles are updated more frequently than others, the initial swarm size is not always optimal).

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Copyright © 2008 Laura Dioşan and Mihai Oltean. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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