Particle Swarms: Optimization Based on Sociocognition

Particle Swarms: Optimization Based on Sociocognition

James Kennedy (U.S. Department of Labor, USA)
DOI: 10.4018/978-1-59904-941-0.ch067
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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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