Predator-prey brain storm optimization for DC brushless motor

Brain Storm Optimization (BSO) is a newly-developed swarm intelligence optimization algorithm inspired by a human being's behavior of brainstorming. In this paper, a novel predator-prey BSO model, which is named Predator-prey Brain Storm Optimization (PPBSO), is proposed to solve an optimization problem modeled for a DC brushless motor. The Predator-prey concept is adopted to better utilize the global information and improve the swarm diversity during the evolution process. The proposed algorithm is applied to solve the optimization problems in an electromagnetic field. The comparative results demonstrate that both PPBSO and BSO can succeed in optimizing design variables for a DC brushless motor to maximize its efficiency. Simulation results show PPBSO has better ability to jump out of local optima when compared with the original BSO. In addition, it demonstrates satisfactory stability in repeated experiments.