Abstract Integrated with the basic principle of particle swarm optimization, a rapid evolutionary algorithm is proposed based on the characteristics of the cloud model on the process of transforming a qualitative concept to a set of quantitative numerical values, namely cloud hypermutation particle swarm optimization algorithm. Its core idea is to achieve the evolution of the learning process and the mutation operation by the normal cloud particle operator. With the cloud model, inheritance and mutation of the particle can be modeled naturally and uniformly, which makes it easy and nature to control the scale of the searching space. The simulation results show that the proposed algorithm has fine capability of finding global optimum, especially for multimodal function.
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2011, Vol. 24 
