An Improved Particle Swarm Optimization with Mutation Based on Similarity

Particle swarm optimization (PSO) is a new population-based intelligence algorithm and exhibits good performance on optimization. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In this paper, a new conception, collectivity, is proposed which is based on similarity between the particle and the current global best particle in the swarm. And the collectivity was used to randomly mutate the position of the particles, which make swarm keep diversity in the search space. Experiments on benchmark functions show that the new algorithm outperforms the basic PSO and some other improved PSO.