An Improved Particle Swarm Algorithm for Search Optimization

被引：2

作者：

Li Zhi-jie ^{[1
]}

Liu Xiang-dong ^{[1
]}

Duan Xiao-dong ^{[1
]}

Wang Cun-rui ^{[1
]}

机构：

[1] Dalian Nationalities Univ, Res Inst Nonlinear Informat Technol, Dalian 116600, Peoples R China

来源：

PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL I | 2009年

关键词：

D O I：

10.1109/GCIS.2009.40

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To address the problem of space locus searching, a slowdown particle swarm optimization (SPSO) is proposed to improve the convergence performance of particle swarm from the position viewpoint. The particle swarm in SPSO is divided into many Independent sub-swarms to guarantee that particles convergent to different position, since space locus has multiple optimal solutions and requires the convergence of both fitness and position of particle Furthermore, particle velocity is updated by half according to fitness to achieve the position convergence The simulation results show the advantage of the proposed slowdown particle swarm optimization-SPSO, which leads to an efficient position convergence.

引用

页码：154 / 158

页数：5

共 13 条

[1]

Angeline P., 1998, Seventh Annual Conference on Evolutionary Programming, San Diego, USA, 25 -27 Mar 1998, P601, DOI DOI 10.1007/BFB0040753

[2]

[Anonymous], 1991, Handbook of genetic algorithms

[3]

[Anonymous], P 7 INT C EV PROGR

[4]

BOX GEP, 1993, J ROY STAT SOC, V6, P81

[5]

CARLISLE A, 2000, P INT C ART INT, P11

[6]

DUAN XD, 2005, PARTICLE SWARM OPTIM

[7]

Fu Shao-chang, 2007, Journal of System Simulation, V19, P1562

[8]

Goldberg D. E., 1989, Genetic algorithms in machine learning, search and optimization

[9]

GROSAN C, 2005, P 4 MEX INT C ART IN, P623

[10]

GROSAN C, 2004, P WORLD COMP C, P365

← 1 2 →