A multi-swarm particle swarm optimization algorithm characteristic of optimum mutation

With respect to the inherent deficiency in the particle swarm optimization algorithm,a novel multi-swarm particle swarm optimization algorithm with optimum mutation is presented in this paper.By applying the mutation operator to the best particle in the sub-warm,the objective function can be optimized by the algorithm,which can prevent premature convergence and has better convergence and local exploitation ability.Experiments are conducted on a set of benchmark functions and the results show that it can break away from the attraction of the local optimal solution and thus achieve excellent results in fewer generations.