A Novel PSO-DE-Based Hybrid Algorithm for Global Optimization

被引：0

作者：

Niu, Ben ^{[1
]}

Li, Li ^{[1
]}

机构：

[1] Shenzhen Univ, Sch Management, Shenzhen 518060, Peoples R China

来源：

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2008年 / 5227卷

关键词：

Particle swarm optimization; differential evolution; global optimization;

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：

This paper presents a new hybrid global optimization algorithm PSODE combining particle swarm optimization (PSO) with differential evolution (DE). PSODE is a type of parallel algorithm, in which PSO and DE are executed in parallel to enhance the population with frequent information sharing. To demonstrate the effectiveness of the proposed algorithm, four benchmark functions are performed, and the performance of the proposed algorithm is compared to PSO and DE to demonstrate its superiority.

引用

页码：156 / 163

页数：8

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