A particle swarm optimization with differential evolution

Abstract

Particle swarm optimization(PSO) is a simple population-based algorithm which has many advantages such as simple operation and converge quickly. However, PSO is easily trapped into local optimum. Differential evolution(DE) is a simple evolutionary algorithm the same as PSO. This paper proposes an improved PSO algorithm based on DE operator(termed IPSODE). Finally, several benchmark functions are used to evaluate the performance of the proposed IPSODE algorithm. The simulation results show the stability and the effectiveness of IPSODE algorithm on the optimum search, also demonstrate that the performance of the IPSODE is better than the standard algorithm in solving the benchmark functions. © 2011 Springer-Verlag.