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A Novel Dynamic Particle Swarm Optimization Algorithm Based on Chaotic Mutation

A novel dynamic particle swarm optimization algorithm based on chaotic mutation (DCPSO) is proposed to solve the problem of the premature and low precision of the common PSO. Combined with linear decreasing inertia weight, a kind of convergence factor is proposed based on the variance of the population’s fitness in order to adjust ability of the local search and global search; The chaotic mutation operator is introduced to enhance the performance of the local search ability and to improve the search precision of the new algorithm. The experimental results show finally that the new algorithm is not only of greater advantage of convergence precision, but also of much faster convergent speed than those of common PSO (CPSO) and linear inertia weight PSO (LPSO).