An improved PSO algorithm based on particle exploration for function optimization and the modeling of chaotic systems

A novel method to improve the global performance of particle swarm optimization (PSO) is proposed, which extends the exploring domain of the optimal position in the current generation and the optimal position thus achieved by every particle. In each generation, the best two positions are modified according to their searching radii and directions. If the new solutions improve the old ones, the optimal positions in the updating equations of the conventional PSO algorithm will be replaced by the new solutions. Using this operator, the swarm diverges from local optimization easily. Moreover, the algorithm is easily implemented, and because the basic structure of PSO is not altered, the algorithm can be easily combined with different PSO methods to improve the performance. Some benchmark functions and chaotic systems are evaluated via simulations, showing that the proposed algorithm exceeds existing methods such as BPSO, LDWPSO, DNLPSO to some extent.