Particle Swarms
- aU.S. Department of Labor, USA
- ✉Corresponding author
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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.
TL;DRThis chapter develops the particle swarm algorithm from the sociocognitive perspective, describes some variations of the algorithm, and draws comparisons between particle swarms and related paradigms. (Semantic Scholar)