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Indexed by:期刊论文

Date of Publication:2013-04-01

Journal:JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS

Included Journals:SCIE、EI、Scopus

Volume:24

Issue:2

Page Number:324-334

ISSN No.:1004-4132

Key Words:particle swarm optimization (PSO); chaos theory; cloud model; hybrid optimization

Abstract:As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization (PSO) is applied to solve high-dimensional multi-modal functions, a hybrid optimization algorithm based on the cat mapping, the cloud model and PSO is proposed. While the PSO algorithm evolves a certain of generations, this algorithm applies the cat mapping to implement global disturbance of the poorer individuals, and employs the cloud model to execute local search of the better individuals; accordingly, the obtained best individuals form a new swarm. For this new swarm, the evolution operation is maintained with the PSO algorithm, using the parameter of pop_distr to balance the global and local search capacity of the algorithm, as well as, adopting the parameter of mix_gen to control mixing times of the algorithm. The comparative analysis is carried out on the basis of 4 functions and other algorithms. It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions. Finally, the suggested values are proposed for parameters pop_distr and mix_gen applied to different dimension functions via the comparative analysis of parameters.