Chaos cultural particle swarm optimization and its application

Abstract

A new version of the classical particle swarm optimization (PSO), namely, Chaos culture particle swam optimization (CCPSO), is proposed to overcome the shortcoming of the premature of the classical PSO. The proposed algorithm integrates PSO with the framework of cultural algorithm model. PSO is utilized as the evolution method of population space. Meanwhile, the chaotic search operator is imported to build the knowledge structure of belief space, with which guiding the evolution process of the proposed algorithm, moving particles to the global optimal solution can be more effective. Then, the proposed algorithm is tested with typical test functions. The result shows that the global searching ability of CCPSO is better than that of PSO. In the last part of the paper, CCPSO was applied to the optimal operation of cascade hydropower station. The operation result shows the feasibility and high efficiency of the proposed algorithm, while compared with tradition method, CCPSO is faster and has the higher precision. Therefore a new method is proposed. © 2009 Springer Berlin Heidelberg.