A Hybrid Algorithm Based on NSGA-II and MOPSO for Multi-Objective Designs of Electromagnetic Devices
Yilun Li, Zhengwei Xie, Shiyou Yang, Zhuoxiang Ren
Abstract
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.
Topics & Concepts
SortingBenchmark (surveying)CrossoverComputer scienceAlgorithmInitializationParticle swarm optimizationGenetic algorithmHybrid algorithm (constraint satisfaction)Meta-optimizationSelection (genetic algorithm)Mathematical optimizationMutationMulti-objective optimizationMathematicsArtificial intelligenceMachine learningGeographyConstraint logic programmingProbabilistic logicConstraint satisfactionChemistryBiochemistryGeneGeodesyProgramming languageAdvanced Multi-Objective Optimization AlgorithmsOptimal Experimental Design MethodsMetaheuristic Optimization Algorithms Research