一种带停滞信息的自适应粒子群优化方法

doi:10.3969/j.issn.1001-2400.2016.03.021

Abstract

Abstract:

To improve the performance of the particle swarm optimization algorithm, the optimal network of the particle age structure with stagnation information is designed, and the information about this network is used to adaptively change the three key parameters of the particle swarm optimization algorithm. At the same time, an adaptive particle swarm optimization method with stagnancy information is proposed and specific optimization steps of this method are given. Four classical low and high dimension benchmark test functions are used to validate the performance of the optimization method, and a comparison study is made with gravitational search algorithm and the traditional particle swarm optimization algorithm without stagnancy information. The comparison study shows that the search efficiency of the proposed method is 2 times higher than that of other methods in the literature in the case of low dimensional multimodal functions. When the dimension of functions is higher than 2, the search efficiency of the proposed method is almost the same as that of other methods, but with the better ability to achieve global solution and local solutions, and the higher solving precision.

Key words: stagnancy, particle swarm optimization, multimodal function optimization, self-adaptive adjust tactics

CLC Number:

TP202+.7

LIU Daohua1;CHEN Liangqiong2;HU Xiuyun1;ZHANG Qian1. Adaptive particle swarm optimization method with stagnancy information[J].Journal of Xidian University, 2016, 43(3): 120-124+160.

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Adaptive particle swarm optimization method with stagnancy information

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