Due to the simple but effective search framework, differential evolution (DE) has achieved successful applications in multiobjective optimization problems. However, most of the previous research on the multiobjective DE (MODE) focused on the design of control strategies of parameters and mutation operators for a given population at each generation, and ignored that the given population might have a bad distribution in the objective space. Therefore, this paper proposes a new variant of MODE in which a reference axis vicinity mechanism (RAVM) is developed to restore the good distribution of the given population and maintain its convergence before the evolution (i.e., mutation, crossover, and selection) starts at each generation. Besides the RAVM, a hybrid control strategy of parameters and mutation operators is also presented to accelerate convergence by integrating both randomness and guided information derived from solutions generated during the search process. Computational results on four series of benchmark problems illustrate that the proposed MODE with the RAVM and hybrid control strategy is competitive or even superior to some state-of-the-art multiobjective evolutionary algorithms in the literature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.