Abstract
Dynamic multi-objective optimization problems involve multiple conflicting and time-dependent objectives that change continuously depending on the environment. Therefore, effectively tracking the movement of the Pareto-optimal front and the Pareto-optimal set (PS) using a single strategy is difficult. In this study, we propose an ensemble method called EMCD, which is based on the characterization of dynamism. EMCD selects different strategies to generate new populations depending on the characteristics of environmental changes. In our proposed algorithm, historical information is used to locate new populations when the current and historical environmental changes are similar. Otherwise, EMCD establishes whether the PS has changed. If the PS changes, individuals from the population of the new environment are generated by using a set of knee points based on the two previous consecutive populations. If not, new populations are generated by using the perturbation-based approach. In addition, we use an adaptive diversity introduction strategy to ensure the solvability of our proposed algorithm in dynamic environments. To verify the effectiveness of EMCD, we compare its performance to that of three state-of-the-art algorithms on fourteen benchmark problems. The experimental results show that EMCD outperforms the comparison algorithms on most of the benchmark problems.
Published Version
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