期刊
INFORMATION SCIENCES
卷 176, 期 8, 页码 937-971出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2005.02.003
关键词
particle swarm optimization; particle trajectories; equilibrium; convergence
Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO Studies are empirical, with only a few theoretical analyses that concentrate on understanding particle trajectories. These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current theoretical studies, and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term. The paper also provides a formal proof that each particle converges to a stable point. An empirical analysis of multidimensional stochastic particles is also presented. Experimental results are provided to support the conclusions drawn from the theoretical findings. (C) 2005 Elsevier Inc. All rights reserved.
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