An Improved Particle Swarm Optimization Algorithm

被引：1
|
作者：
Chang, Chunguang [1 ]
Wu, Xi [1 ]
机构：
[1] Shenyang Jianzhu Univ, Sch Management, Shenyang 110168, Liaoning, Peoples R China
来源：
CYBER SECURITY INTELLIGENCE AND ANALYTICS | 2020年 / 928卷
基金：
中国国家自然科学基金;
关键词：
PSO; Crossover operator; PSOCO;
D O I：
10.1007/978-3-030-15235-2_195
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on a brief introduction to the principle of particle swarm optimization (PSO) and different improved methods of PSO at home and abroad, the particle swarm optimization algorithm is improved for the short-comings of particle swarm optimization. This paper selects the particle velocity formula with a learning model, which has good exploration ability and can effectively avoid the algorithm falling into local optimum, and recon figure some parameters of the algorithm. A PSO with two crossover operations (PSOCO) is put forward, and the next research direction of PSOCO algorithm is given.
引用
收藏
页码：1406 / 1410
页数：5
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