Self-active inertia weight strategy in particle swarm optimization algorithm

被引：0

作者：

Chen, Guimin ^{[1
]}

Min, Zhengfeng ^{[1
]}

Jia, Jianyuan ^{[1
]}

Huang, Xinbo ^{[2
]}

机构：

[1] Xidian Univ, Sch Elect & Mech Engn, Xian 710071, Peoples R China

[2] Xian Univ Engn Sci & Technol, Sch Electromech Engn, Xian 710071, Peoples R China

来源：

WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年

关键词：

particle swarm optimization; inertia weight;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Inertia weight is one of the most important parameters of particle swarm optimization (PSO) algorithm. We introduce s self-active inertia weight strategy, in which the inertia weight is updated according to the convergence rate of the search process related to the optimized function. Four different functions were used to evaluate the effects of these strategies on the PSO performance. The experimental results show that self-active strategy is significantly faster convergence than LPSO.

引用

页码：3686 / +

页数：2

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