A Novel Hybrid Multi-Objective Optimization Algorithm and Its Application to Designs of Electromagnetic Devices

Abstract

In this article, a novel hybrid multi-objective optimization (MOO) algorithm is proposed by combining an improved sparrow search algorithm (SSA) with an improved non-dominated sorting genetic algorithm (NSGA-II). The original SSA is improved by the introduction of population updating mechanism of moth-flame optimization (MFO) algorithm and by adopting adaptive mutation; meanwhile, NSGA-II is enhanced by using Latin hypercube sampling and dynamical selection mechanism of crossover and mutation operators. The performance of the proposed hybrid algorithm is verified using standard test functions and it is applied to the multi-objective optimal designs of TEAM22 benchmark problem and topology optimization problem of an electromagnetic actuator prototype. Numerical results demonstrate the effectiveness and superiority of the proposed algorithm.