Hybrid Particle Swarm Optimisation Based on History Information Sharing

被引：0

作者：

Fu, Wenlong ^{[1
]}

Johnston, Mark ^{[1
]}

Zhang, Mengjie ^{[2
]}

机构：

[1] Victoria Univ Wellington, Sch Math Stat & Operat Res, Wellington, New Zealand

[2] Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington, New Zealand

来源：

GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2011年

关键词：

Particle Swarm Optimisation; Multi-Agent; Local Search; Function Optimisation; DIFFERENTIAL EVOLUTION;

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 Optimisation (PSO) is an intelligent search method based on swarm intelligence and has been widely used in many fields. However it is also easily trapped in local optima. In order to find a global optimum, some evolutionary search operators used in multi-agent genetic algorithms are integrated into a novel hybrid PSO, with the expectation of effectively escaping from local optima. Particles share their history information and then update their positions using the latest and best history information. Some benchmark high-dimensional functions (from 20 to 10000 dimensions) are used to test the performance of the hybrid algorithms. The results demonstrate that the algorithm can solve high-dimensional nonlinear optimisation problems and that the number of function evaluations required to do so increases with function dimension at a sublinear rate.

引用

页码：77 / 84

页数：8

共 19 条

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