Gravitational particle swarm

被引：49

作者：

Tsai, Hsing-Chih ^{[1
]}

Tyan, Yaw-Yauan ^{[2
]}

Wu, Yun-Wu ^{[3
]}

Lin, Yong-Huang ^{[1
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Construct Engn, Taipei, Taiwan

[2] China Univ Technol, Dept Civil Engn & Hazard Mitigat Design, Hu Kou, Taiwan

[3] China Univ Technol, Dept Architecture, Taipei, Taiwan

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2013年 / 219卷 / 17期

关键词：

Optimization; Particle swarm optimization; Gravitational search algorithm; Gravitational particle swarm; OPTIMIZATION; ALGORITHM;

D O I：

10.1016/j.amc.2013.03.098

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Particle swarm optimization (PSO) is inspired by social behavior of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity, and both of them are related to swarm intelligence (SI). Gravitational particle swarm (GPS) is proposed where a GPS agent has attributes of GSA and PSO. GPS agents update their respective positions with PSO velocity and GSA acceleration. GPS agents, therefore, are able to exhibit PSO bird social and cognitive behaviors and motion in flight, while also reflecting the law of gravity of GSA. From results of 23 benchmark functions, GPS does significantly improve PSO and GSA, with noticeably marked improvements. This paper proposes GPS for hybridizing PSO and GSA due to the outstanding performance and interesting concepts embodied in the GPS. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：9106 / 9117

页数：12

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