Adaptive particle swarm optimization algorithm based on cloud theory
Dexiang Luo · Computer Engineering and Applications Journal · 2009
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.