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A multipopulation evolutionary framework with Steffensen's method for dynamic multiobjective optimization problems
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DOI：10.1007/s12293-021-00348-3.png)
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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.
Keyword:
Dynamic multiobjective optimization
Steffensen's method
Multipopulation structure
Prediction strategy
Diversity-maintenance strategy
期刊
IF:
2.3
论文数:
458
被引数:
718
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引用论文
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
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分享收藏A directed search strategy for evolutionary dynamic multiobjective optimization.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
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