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.

[1]  W. Renhart,et al.  Pareto optimality and particle swarm optimization , 2004, IEEE Transactions on Magnetics.

[2]  A. Nicolas,et al.  Efficient genetic algorithms for solving hard constrained optimization problems , 2000 .

[3]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[5]  L dos Santos Coelho,et al.  Gaussian Artificial Bee Colony Algorithm Approach Applied to Loney's Solenoid Benchmark Problem , 2010, IEEE Transactions on Magnetics.

[6]  Stephane Brisset,et al.  Analytical model for the optimal design of a brushless DC wheel motor , 2005 .

[7]  Stephane Brisset,et al.  Comparison of Two Multi-Agent Algorithms: ACO and PSO for the Optimization of a Brushless DC Wheel Motor , 2008 .

[8]  Fang Liu,et al.  Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning , 2010 .

[9]  L. Lebensztajn,et al.  Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach , 2012, IEEE Transactions on Magnetics.

[10]  Shuhong Wang,et al.  Dynamic Multilevel Optimization of Machine Design and Control Parameters Based on Correlation Analysis , 2010, IEEE Transactions on Magnetics.

[11]  G. Crevecoeur,et al.  A Two-Level Genetic Algorithm for Electromagnetic Optimization , 2010, IEEE Transactions on Magnetics.

[12]  Zhi-hui Zhan,et al.  A modified brain storm optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[13]  Chang-Hwan Im,et al.  Hybrid genetic algorithm for electromagnetic topology optimization , 2003 .

[14]  Yuhui Shi,et al.  An Optimization Algorithm Based on Brainstorming Process , 2011, Int. J. Swarm Intell. Res..

[15]  Yuhui Shi,et al.  Brain Storm Optimization Algorithm , 2011, ICSI.

[16]  Atsushi Ishigame,et al.  Particle Swarm Optimization Considering the Concept of Predator-Prey Behavior , 2006, 2006 IEEE International Conference on Evolutionary Computation.