Improved Particle Swarm Optimiz ation Algorithm Based on Cloud Model T heory
DONG Hongchen
ZHANG Qiang
ZHANG Wanyang
Abstract：Particle swarm optimization algorithm for optimization in function easily falls into local optimal solution and the premature quickly converges of such shortcomings .Combined with the excellent characteristics of cloud model transfor-mation between qualitative and quantitative ,an improved particle swarm optimization algorithm based on cloud model theory is proposed .The idea is to initialize the population through reverse learning mechanism ,to better solve value around the global best individual and its optimal individual in PSO by the normal cloud particle operator .Finally ,the individual particles are mutation to jump out of local optimal solution by using the theory of chaos .The simulation results show that the pro-posed algorithm has fine capability of finding global optimum ,especially multi peak function .
Keywords：particle swarm optimizationcloud modelchaosoptimization
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 1123-1126 )