Improved Particle Swarm Optimization algorithm in dynamic environment

Author

Changcheng Xiang ; Xuegang Tan ; Yi Yang

Author_Institution

Key Lab. of Biologic Resources Protection & Utilization, Hubei Minzu Univ., Enshi, China

fYear

2014

fDate

May 31 2014-June 2 2014

Firstpage

3098

Lastpage

3102

Abstract

In this paper, The improved Particle Swarm Optimization in dynamic objective function environment (DOFPSO) is purposed. The dynamic environment will change with the time t. The DOFPSO algorithm discuss that how to determine changes of the time (environment) and how to keep population diversity. The improved algorithm has the ability to fast response the change of environment and could find the best fitness value quickly. The results of experiment indicate that DOFPSO is more effective than particle swarm optimization (PSO) and restart method particle swarm optimization (RMPSO) in the response of change of environment and fast convergence.

Keywords

convergence; environmental factors; particle swarm optimisation; DOFPSO algorithm; RMPSO; dynamic objective function environment; environment change; fast convergence; fitness value; improved particle swarm optimization algorithm; population diversity; restart method particle swarm optimization; Convergence; Heuristic algorithms; Linear programming; Optimization; Particle swarm optimization; Sociology; Statistics; Convergence; Dynamic Environment; Particle Swarm Optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (2014 CCDC), The 26th Chinese

Conference_Location

Changsha

Print_ISBN

978-1-4799-3707-3

Type

conf

DOI

10.1109/CCDC.2014.6852707

Filename

6852707