A parallel particle swarm optimization algorithm

被引：0

作者：

Ma, Yan ^{[1
]}

Sun, Jun ^{[1
]}

Xu, Wenbo ^{[1
]}

机构：

[1] Southern Yangtze Univ, Inst Informat Technol, Wuxi 214122, Peoples R China

来源：

DCABES 2006 PROCEEDINGS, VOLS 1 AND 2 | 2006年

关键词：

particle swarm optimization algorithm; parallel; parallel particle swarm optimization; function test;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Particle swarm optimization algorithm is a newly proposed population -based algorithm. Although efficient in many optimization problems, it may encounter the problem of premature convergence and computational time consumption. In this paper, we attempt to introduce parallel mechanism into PSO and proposes PPSO (Parallel PSO) algorithm. We test the PPSO on four widely known benchmark functions and the experiment results show the efficiency and efficacy of PPSO.

引用

页码：61 / 64

页数：4

共 4 条

[1]

Eberhart RC, 2001, IEEE C EVOL COMPUTAT, P81, DOI 10.1109/CEC.2001.934374

[2]

Kennedy J., 1998, Evolutionary Programming VII. 7th International Conference, EP98. Proceedings, P581

[3]

Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968

[4] The merits of a parallel genetic algorithm in solving hard optimization problems [J].

van Soest, AJK ;

Casius, LJRR .

JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME, 2003, 125 (01) :141-146

← 1 →