A Modified Particle Swarm Optimization Algorithm
Ai-Qin Mu, De-Xin Cao, Xiao-Hua Wang
DOI: 10.4236/ns.2009.12019   PDF    HTML     10,291 Downloads   23,226 Views   Citations


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.

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Mu, A. , Cao, D. and Wang, X. (2009) A Modified Particle Swarm Optimization Algorithm. Natural Science, 1, 151-155. doi: 10.4236/ns.2009.12019.

Conflicts of Interest

The authors declare no conflicts of interest.


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