Abstract
Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This paper presents a dynamic multiobjective evolutionary framework (DMOEF-MS), which adopts a novel multipopulation structure and Steffensen’s method to solve DMOPs. In DMOEF-MS, only one population deals with the original DMOP, while the others focus on single-objective problems that are generated by the weighted summation of the original DMOP. Then, Steffensen’s method is used to control the evolving process in two ways: prediction and diversity-maintenance. Particularly, the prediction strategy is devised to predict the next promising positions for the individuals that handle single-objective problems, and the diversity-maintenance strategy is used to increase population diversity before the environment changes and reinitialize the multiple populations after the environment changes. This paper gives a comprehensive comparison of DMOEF-MS with some state-of-the-art DMOEAs on 14 DMOPs and the experimental results demonstrate the effectiveness of the proposed algorithm.
Highlights
With multiple conflicting objectives, multiobjective optimization problems (MOPs) [1] have been successfully solved by various evolutionary algorithms (EAs), such as NSGA-II [2], SPEA2 [3], MOPSO [4], multiobjective evolutionary algorithms (MOEAs)/D [5], ACO [6], and so forth
This paper proposes a multipopulation framework, in which one population deals with the original dynamic multiobjective optimization problems (DMOPs) and the others handle the single-objective problems that are generated by the weighted summation method
For memory-based strategies, the basic principle is reusing past information to improve the performance of a dynamic multiobjective evolutionary algorithm (DMOEA) in the new environment after changes and memory-based strategies are suitable for DMOPs with periodically changing environments
Summary
Multiobjective optimization problems (MOPs) [1] have been successfully solved by various evolutionary algorithms (EAs), such as NSGA-II [2], SPEA2 [3], MOPSO [4], MOEA/D [5], ACO [6], and so forth. A multipopulation framework which cooperates with Steffensen’s method [30], namely DMOEFMS, is introduced to solve DMOPs. In many existing multipopulation-based methods, the multiple populations cooperate to explore the searching space and focus on the same DMOP. In many existing multipopulation-based methods, the multiple populations cooperate to explore the searching space and focus on the same DMOP In this case, each population evolves with less diversity-related guiding information obtained from its optimization problem. This paper proposes a multipopulation framework, in which one population deals with the original DMOP and the others handle the single-objective problems that are generated by the weighted summation method. The prediction strategy is devised to predict the new location of the individuals that handle single-objective problems, and the diversity-maintenance strategy is used to increase the population diversity at fixed intervals of generations and reinitialize the multiple populations after the environment changes.
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