Multiguiders and Nondominate Ranking Differential Evolution Algorithm for Multiobjective Global 
Optimization of Electromagnetic Problems

Abstract

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.