Particle swarm optimization algorithm: an overview

Soft ComputDOI swarm optimization algorithm: an overviewDongshu Wang1 Dapei Tan1 Lei Liu2 Springer-Verlag Berlin Heidelberg 2017 AbstractParticle swarm optimization (PSO) is apopulation-based stochastic optimization algorithm moti-vated by intelligent collective behavior of some animals suchas flocks of birds or schools of fish. Since presented in 1995, ithas experienced a multitude of enhancements. As researchershave learned about the technique, they derived new versionsaiming to different demands, developed new applications in ahostofareas,publishedtheoreticalstudies oftheeffectsofthevarious parameters and proposed many variants of the algo-rithm. This paper introduces its origin and background andcarries out the theory analysis of the PSO. Then, we analyzeits present situation of research and application in algorithmstructure, parameter selection, topology structure, discretePSO algorithm and parallel PSO algorithm, multi-objectiveoptimization PSO and its engineering applications.

on swarm, in which each individual is called a particle defined as a potential solution of the optimized problem in D-dimensional search space, and it can memorize the opti-mal position of the swarm and that of its own, as well as the velocity.Ineachgeneration,theparticlesinformationiscom-

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