期刊
INFORMATION PROCESSING LETTERS
卷 102, 期 1, 页码 8-16出版社
ELSEVIER
DOI: 10.1016/j.ipl.2006.10.005
关键词
particle swarm optimization; analysis of algorithms; stochastic convergence analysis; stochastic optimization; parameter selection
This letter presents a formal stochastic convergence analysis of the standard particle swarm optimization (PSO) algorithm, which involves with randomness. By regarding each particle's position on each evolutionary step as a stochastic vector, the standard PSO algorithm determined by non-negative real parameter tuple {omega, c(1), c(2)) is analyzed using stochastic process theory. The stochastic convergent condition of the particle swarm system and corresponding parameter selection guidelines are derived. (c) 2006 Elsevier B.V. All rights reserved.
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