PARTICLE SWARM OPTIMIZATION BASED ON WEIGHTED AGGREGATION DEGREE AND ADAPTIVE DECISION

被引：0

作者：

Wan, Renxia ^{[1
]}

Zhu, Lijun ^{[2
]}

Liu, Kai ^{[3
]}

Chen, Ruidian ^{[4
]}

机构：

[1] North Minzu Univ, Ningxia Key Lab Intelligent Informat & Big Data P, Beijing, Peoples R China

[2] North Minzu Univ, Coll Math & Informat Sci, Beijing, Peoples R China

[3] Pinghu Coll, Math Grp, Shanghai, Peoples R China

[4] Fujian Hongyang Software Co LTD, Fuzhou, Fujian, Peoples R China

来源：

UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN-SERIES A-APPLIED MATHEMATICS AND PHYSICS | 2020年 / 82卷 / 01期

基金：

中国国家自然科学基金;

关键词：

particle swarm optimization; similarity; weighted aggregation; adaptive decision; PSO VARIANT;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In response to the problem that particle swarm optimization algorithm (PSO) is prone to falling into local optimum and premature convergence in later operations, this paper reconstructs the concept of weighted aggregation based on a redefined similarity to describe the degree of diversity of the population, and adjusts the particle searching space with an adaptive decision to improve the global searching ability of PSO. Optimization ability, convergence speed and stability of the particle swarm algorithm are finally effectively improved. The experimental analysis further shows the effectiveness of the algorithm.

引用

页数：10

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