A new diversity guided particle swarm optimization with mutation

This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing premature convergence. Beta distribution is used to perform the mutation in the proposed BMPSO algorithm. The performance of the BMPSO algorithm is investigated on a set of ten standard benchmark problems and the results are compared with the original PSO algorithm. The numerical results show that the proposed algorithm outperforms the basic PSO algorithm in all the test cases taken in this study.