Hybrid particle swarm - Evolutionary algorithm for search and optimization

被引:0
|
作者
Grosan, C [1 ]
Abraham, A
Han, SY
Gelbukh, A
机构
[1] Univ Babes Bolyai, Dept Comp Sci, R-3400 Cluj Napoca, Romania
[2] Chung Ang Univ, Sch Comp Sci & Engn, Seoul 156756, South Korea
[3] IPN, CIC, Mexico City 07738, DF, Mexico
关键词
D O I
暂无
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 Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO - evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.
引用
收藏
页码:623 / 632
页数:10
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