Title :
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
fDate :
May 31 2014-June 2 2014
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;
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
DOI :
10.1109/CCDC.2014.6852707
