A new stochastic particle swarm optimizer

被引:23
作者
Cui, ZH [1 ]
Zeng, JC [1 ]
Cai, XJ [1 ]
机构
[1] Taiyuan Heavy Machinery Inst, Div Syst Simulat & Comp Applicat, Shanxi 030024, Peoples R China
来源
CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 | 2004年
关键词
D O I
10.1109/CEC.2004.1330873
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Particle Swarm Optimizer is a novel algorithm where a population of candidate problem solution vectors evolves "social" norms by being influenced by their topological neighbors. The standard Particle Swarm Optimizer(PSO) may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. A new particle swarm optimizer, called stochastic PSO (SPSO), which is combined with Tabu technique, is presented based on the analysis of the standard PSO. And because of the its local search capability, the SPSO is more efficiency. And the global convergence analysis is made using the F.Solis and R.Wets' research results. Finally, several examples are simulated to show that SPSO is more efficient than the standard PSO.
引用
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页码:316 / 319
页数:4
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