Improving particle swarm algorithm with cognitive information-based parameter tuning

This paper presents an improved particle swarm optimization (PSO) algorithm, which utilizes the cognitive information (Pbests) of the individuals in the particle swarm to tune the values of parameters during the PSO evolutionary process. The proposed approach has been applied to solve several benchmark function optimization problems and the performance is compared with several existing PSO algorithms, the testing result demonstrates that the proposed method can outperform the other PSOs and obtain a better solution.