Many-objective brain storm optimization algorithm

Convergence and diversity are two core indicators of multi-objective optimization. The optimization and the balance of them are the keys of multi-objective evolutionary algorithms (MOEA). As a new kind of swarm intelligence optimization algorithm, brain storm optimization (BSO) has paid more attention of more researchers in different fields. Based on the research of the existing multi-objective BSO (MOBSO), this paper optimizes the convergence and diversity by analyzing the decision variables of problems. Decomposition strategy is carried out to increase the select pressure while the convergence optimization is performed, and the strategy of reference points is adopted to update the population to increase the diversity while the diversity optimization is performed. Finally, we extend and propose the many-objective brain storm optimization algorithm (MaOBSO). In addition, this paper proposes a new adaptive clustering strategy based on the corner points, which could clarify the orientation of individuals and improve the expansibility of population. Compared with several existing multi-objective evolutionary algorithms with better performance, a large number of simulation results show that the algorithm of this paper has excellent performance. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.