Adaptive Parameter Controlling Non-Dominated Ranking Differential Evolution for Multi-Objective Optimization of Electromagnetic Problems

This paper proposes an adaptive parameter controlling non-dominated ranking differential evolution (A-NRDE) algorithm for multi-objective optimal design of electromagnetic problems. The variable parameters, such as mutation and crossover rates, are self-controlled based on the information of successful individuals and the number of Pareto optimal solutions in current iteration. In mutation step, the proposed algorithm incorporates multi-guiders to obtain a uniformly distributed Pareto front; the advantages of DE are combined with the mechanisms of non-dominated ranking and crowding distance sorting. The proposed A-NRDE algorithm is applied to a multi-objective version of TEAM 22 and five benchmark problems. Experimental results show that the proposed our approach is able to obtain a good distribution of Pareto front and convergence in all cases. Compared with several other state-of-the-art evolutionary algorithms, it achieves not only comparable results in terms of convergence and diversity metrics, but also a considerable reduction of the computational effort.