Enhanced Brain Storm Optimization with Role-playing Strategy

Brain storm optimization (BSO) is a kind of swarm intelligence, which is derived from human brainstorming process. And it has been successfully applied in solving practical problems. As a relatively new algorithm, BSO has the problem of slow convergence speed and being prone to local optimum. In this paper, BSO with role-playing strategy (RPBSO) is proposed. In RPBSO, role-playing strategy is adopted to classify ideas, and idea difference strategy is utilized to generate new ideas. To maintain the diversity of ideas and prevent getting into local optimum, re-initialization operation is adopted as well. RPBSO and several variants of BSO are compared on CEC 2013 benchmark functions. The experimental results show that RPBSO is better than other variants of BSO, which verifies the performance of RPBSO.