What Else Is the Evolution of PSO Telling Us?. (28th January 2008)

• Record Type: Journal Article
• Authors: Dioşan, Laura; Oltean, Mihai
• Other Names: Freitas Alex Academic Editor.
• Is Part Of: Journal of artificial evolution and applications. Volume 2008(2008)
• Publication Date: 2008-01-28
• Subjects: Evolutionary programming (Computer science) -- Periodicals; Evolutionary programming (Computer science); Periodicals; Electronic journals; 006.3823
• DOI: 10.1155/2008/289564
• Languages: English
• ISSNs: 1687-6229

Abstract

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).