Multiobjective Evolution Strategy for Dynamic Multiobjective Optimization

This article presents a novel evolution strategy-based evolutionary algorithm, named DMOES, which can efficiently and effectively solve multiobjective optimization problems in dynamic environments. First, an efficient self-adaptive precision controllable mutation operator is designed for individuals to explore and exploit the decision space. Second, the simulated isotropic magnetic particles niching can guide the individuals to keep uniform distance and extent to approximate the entire Pareto front automatically. Third, the nondominated solutions (NDS) guided immigration can facilitate the population convergence with two different strategies for the NDSs and the dominated solutions, respectively. As a result, our algorithm can track the new approximate Pareto set and approximate Pareto front as quickly as possible when the environment changes. In addition, DMOES can obtain a well-converged and well-diversified Pareto front with much less population size and far lower computational cost. The larger the number of individuals, the sharper the contour of the resulted approximate Pareto front will be. Finally, the proposed algorithm is evaluated by the FDA, dMOP, UDF, and ZJZ test suites. The experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art dynamic multiobjective evolutionary algorithms.