A Hybrid Algorithm Based on NSGA-II and MOPSO for Multi-Objective Designs of Electromagnetic Devices

In this article, a hybrid algorithm is proposed by combining the non-dominated sorting genetic algorithm (NSGA-II) with multi-objective particle swarm optimization (MOPSO) algorithm. The original NSGA-II is improved by using logistic mapping initialization and a dynamic selection mechanism of crossover and mutation operators is proposed. The performance of the proposed hybrid algorithm is verified using standard test functions and it is applied to the multi-objective optimization (MOO) benchmark problem TEAM 22. Numerical results demonstrate the effectiveness and superiority of the proposed hybrid algorithm.