Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm |
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Authors: | M. Jiang Y.P. Luo S.Y. Yang |
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Affiliation: | Department of Automation, Tsinghua University, Beijing 100084, China |
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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 {ω,c1,c2} is analyzed using stochastic process theory. The stochastic convergent condition of the particle swarm system and corresponding parameter selection guidelines are derived. |
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Keywords: | Particle swarm optimization Analysis of algorithms Stochastic convergence analysis Stochastic optimization Parameter selection |
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