Improvement of a multi-objective differential evolution using clustering algorithm

In the last few decades, evolutionary algorithms (EAs) for solving optimization problems have come to the forefront. Because of the complexity of the problem, Multi-objective problems (MOPs) as well as global optimization problem has been developed so far, but parents for genetic reproduction has been considered as one global group in general. In this paper, we apply clustering algorithm to differential evolution (DE) in order to cluster and assign group leaders to the subpopulation for finding optimal solutions as well as guaranteeing population diversity.