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Particle Swarms: Optimization Based on Sociocognition

Particle Swarms: Optimization Based on Sociocognition
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Author(s): James Kennedy (U.S. Department of Labor, USA)
Copyright: 2005
Pages: 35
Source title: Recent Developments in Biologically Inspired Computing
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil) and Fernando J. Von Zuben (State University of Campinas, Brazil)
DOI: 10.4018/978-1-59140-312-8.ch010

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Abstract

Particle swarm optimization is a computer paradigm that is based on human social influence and cognition. Candidate problem solutions are randomly initialized, and improvements are found through interactions among them. Social-psychological aspects of the algorithm are described, followed by implementation details. The particle swarm operates in three kinds of spaces, namely a topological space comprising the “social network” structure of the population, a parameter space of problem variables, and a one-dimensional evaluative space. Variations in the algorithm are described, and finally it is compared to evolutionary computation models.

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