On the convergence analysis and parameter selection in particle swarm optimization

被引：158

作者：

Zheng, YL ^{[1
]}

Ma, LH ^{[1
]}

Zhang, LY ^{[1
]}

Qian, JX ^{[1
]}

机构：

[1] Zhejiang Univ, Dept Control Sci & Engn, Inst Syst Engn, Hangzhou 310027, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年

关键词：

particle swam optimizer; inertia weight; trajectory; convergence;

D O I：

10.1109/ICMLC.2003.1259789

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A PSO with increasing inertia weight, distinct from a widely used PSO with decreasing inertia weight, is proposed in this paper. Far from drawing conclusions from sole empirical study or rule of thumb, this algorithm is derived from particle trajectory study and convergence analysis. Four standard test functions are used to confirm its validity finally. From the experiments, it is clear that a PSO with increasing inertia weight outperforms the one with decreasing inertia weight, both in convergent speed and solution precision, with no additional computing load.

引用

页码：1802 / 1807

页数：6

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