采用扰动加速因子的自适应粒子群优化算法

doi:10.3969/j.issn.1001-2400.2012.04.014

Abstract

Abstract:

The Particle Swarm Optimization (PSO) is an evolutionary method which is used to search for the global optimal solution by iteration. However, PSO has the problem that the particle swarm algorithm falls easily into premature convergence and has a low search efficiency. In this paper, after analyzing several existing improved particle swarm algorithms, a new improved particle swarm algorithm is proposed based on the fact that symmetrical particles distribution can enhance the optimation search results. The proposed algorithm can adjust the inertia weight factor adaptively in different phases of the process according to the variation of the cosine function. In addition, the acceleration coefficents based on linear variation are disturbed under a certain condition. Moreover, an appropriate value of the parameter in this condition is determined via experiments. Several classic functions have been used to test this new algorithm and then the results of this new algorithm are analyzed by comparing it with several relevant algorithms in the literature. The results show that this new algorithm can not only improve the convergence speed significantly, but also improve the premature convergence phenomenon.

Key words: particle swarm optimization, acceleration coefficents, inertia weight factor

CLC Number:

TP301.6

JIANG Jianguo;TIAN Min;WANG Xiangqian;LONG Xiuping;LI Jin. Adaptive particle swarm optimization via disturbing acceleration coefficents[J].J4, 2012, 39(4): 74-80.

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Adaptive particle swarm optimization via disturbing acceleration coefficents

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