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《Computer Engineering and Applications》 2009-01
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Adaptive particle swarm optimization algorithm based on cloud theory

WEI Xing-qiong,ZHOU Yong-quan,HUANG Hua-juan,LUO De-xiang College of Mathematics and Computer Science,Guangxi University for Nationalities,Nanning 530006,China  
In this paper,an adaptive particle swarm optimization algorithm based on cloud theory is proposed,the particles are divided into three group based on the fitness of the particle in order to adopt different inertia weight generating strategy.The inertia weight in general group is adaptively varied depending on X-conditional cloud generator.The inertia weight has the stable tendency and randomness property because of the cloud model,this not only improves the convergence speed,but also maintains the diversity of the population.In all cases studied,CAPSO is greatly superior than PSO in the terms of efficiency.
【Fund】: 国家自然科学基金;; 广西自然科学基金~~
【CateGory Index】: TP301.6
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