A two-stage diversity enhancement differential evolution algorithm for multi-objective optimization problem

Abstract

In order to solve the premature convergence of multi-objective evolutionary algorithm, a two-stage diversity enhancement differential evolution algorithm for multi-objective optimization problem(TSDE) is proposed. The offspring with better performance needs the generation of high-quality parent generation. In this paper, an improved cell density method is used to screen for the high quality parents by estimating the global distribution of the objective space. Moreover, Principal Component Analysis operator is introduced to the external archive to perturb the non-dominated solution, which not only ensures the convergence but also improves the diversity. In order to verify the effectiveness of the algorithm, TSDE and other advanced methods are run on 19 test functions. The results show that TSDE performs better than other algorithms.