%A N. Baatar
%A Minh Trien Pham
%A Chang Seop Koh
%J IEEE Transactions on Magnetics
%T Multiguiders and Nondominate Ranking Differential Evolution Algorithm for Multiobjective Global Optimization of Electromagnetic Problems
%X The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22.
%N 5
%K electrical engineering computing;evolutionary computation;optimisation;MOGO;Testing Electromagnetic Analysis Methods;constraint optimization problems;conventional DE strategies;conventional base vector selection method;electromagnetic problems;multiguider feasibility;multiguiders;multiobjective differential evolutionary algorithm;multiobjective evolutionary algorithm;multiobjective global optimization;multiobjective optimizers;mutation step;nondominate ranking differential evolution algorithm;nondominated solutions;nondominated sorting method;performance constrain problems;real-world problem;secondary population;single-objective problems;Differential evolution;Testing Electromagnetic Analysis Methods (TEAM) problem 22;multiguiders;multiobjective optimization;nondominated ranking
%P 2105-2108
%V 49
%D 2013
%I IEEE
%L SisLab2493