Open AccessA Modified Particle Swarm Optimization Algorithm
Author(s) -
Aiqin Mu,
Cao De-xin,
Tiesong Hu
Publication year - 2009
Publication title -
natural science
Language(s) - English
Resource type - Journals
eISSN - 2150-4105
pISSN - 2150-4091
DOI - 10.4236/ns.2009.12019
Subject(s) - particle swarm optimization , multi swarm optimization , simulated annealing , mathematical optimization , convergence (economics) , algorithm , computer science , meta optimization , premature convergence , metaheuristic , mathematics , economics , economic growth
Particle Swarm Optimization (PSO) is a new optimization algorithm, which is applied in many fields widely. But the original PSO is likely to cause the local optimization with premature convergence phenomenon. By using the idea of simulated annealing algo-rithm, we propose a modified algorithm which makes the most optimal particle of every time of iteration evolving continu-ously, and assign the worst particle with a new value to increase its disturbance. By the testing of three classic testing functions, we conclude the modified PSO algorithm has the better performance of convergence and global searching than the original PSO