An effective hybrid particle swarm optimization with Gaussian mutation. Issue 3 (September 2017) - Lin, Zhensi; Zhang, Qishan
A novel hybrid particle swarm optimization variant is proposed, which combines particle swarm optimization with Gaussian mutation operation based on random strategy. It applies Gaussian mutation on the positions of some randomly selected particles to enhance the search accuracy and convergence speed of swarm. The proposed algorithm can retain the diversity of population and improve the ability of global search. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. The results have been compared with three state-of-the-art particle swarm optimization variants. Experimental results show that the Gaussian mutation and the random select strategy help the proposed algorithm to achieve faster convergence rate and provide better solutions in most of the problems. Further, the new algorithm has been tested on the high-dimensional problems. The results show that the proposed algorithm is not sensitive to high-dimensional problems and can even give a better performance. Moreover, the sensitivity analysis of the parameters was carried out and the setting of the parameters was given.