AUTHORS: Shuyue Wu
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ABSTRACT: Particle swarm optimization algorithm is analyzed base on quantum behaved self learning, particles searching action and local attract point are studied. To the different searching environment in searching progress, the searching actions are divided into four models. The proposed algorithm can self learn the optimization problem, and utilize a suitable learning model, then the whole optimization performance is increased. The comparison and analysis of results with the proposed method and other improved QPSO are given based on CEC2005 benchmark function, the simulation results show the modified algorithm can greatly improve the QPSO performance.
KEYWORDS: Particle Swarm Optimization(PSO), quantum behavior, self learning, local attract point, searching model, evolutionary
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