Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm

Authors:
M. Jiang;Y. P. Luo;S. Y. Yang
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Information Processing Letters
Year:
2007

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Abstract

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 {@w,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.