Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm

被引：248

作者：

Montes de Oca, Marco A. ^{[1
]}

Stutzle, Thomas ^{[1
]}

Birattari, Mauro ^{[1
]}

Dorigo, Marco ^{[1
]}

机构：

[1] Univ Libre Bruxelles, Inst Rech Interdisciplinaires & Dev Intelligence, B-1050 Brussels, Belgium

来源：

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2009年 / 13卷 / 05期

关键词：

Continuous optimization; experimental analysis; integration of algorithmic components; particle swarm optimization (PSO); run-time distributions; swarm intelligence; CONVERGENCE;

D O I：

10.1109/TEVC.2009.2021465

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical study of several PSO variants from a component difference point of view. In the second part of the paper, we propose a new PSO algorithm that combines a number of algorithmic components that showed distinct advantages in the experimental study concerning optimization speed and reliability. We call this composite algorithm Frankenstein's PSO in an analogy to the popular character of Mary Shelley's novel. Frankenstein's PSO performance evaluation shows that by integrating components in novel ways effective optimizers can be designed.

引用

页码：1120 / 1132

页数：13

共 35 条

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