Hybrid approach using GA and PSO for alternator design

Electrical machine design optimization problem can be specified as searching for a compromise between requirements of manufacturers and users. This paper presents hybrid approach using genetic algorithm and particle swarm optimization (HGAPSO) as a modern optimization tool to optimize the alternator design. Three different objective functions are considered separately in this article, namely, the cost, the efficiency and the regulation of alternator. The design parameters of an alternator are optimized by using HGAPSO approach. The proposed method combines two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of alternator design the feasibility of HGAPSO approach is demonstrated on two sample alternators and the results are compared with the traditional PSO method. The results indicate the superiority of the HGAPSO approach over the traditional PSO technique in terms of solution quality and convergence rate.