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A prediction strategy based on special points and multiregion knee points for evolutionary dynamic multiobjective optimization

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Abstract

Dynamic multiobjective optimization problems exist widely in the real word and require the optimization algorithms to track the Pareto front (PF) over time. A prediction strategy based on special points and multi-region knee points (MRKPs) is proposed for solving dynamic multiobjective optimization problems. Whenever a change is detected, the prediction strategy reacts effectively to the change by generating four subpopulations based on four strategies. The first subpopulation is created by selecting the representative individuals using a special point strategy. The second subpopulation consists of a solution set using a multiregion knee point strategy. The third subpopulation is introduced to the nondominated set by a convergence strategy. The fourth subpopulation comprises diverse individuals from an adaptive diversity maintenance strategy. The four subpopulations merge into a new population to accurately predict the location and distribution of the PF after an environmental change. MRKP is compared with four popular evolutionary algorithms on standard instances with different changing dynamics. Finally, MRKP provides better results than other competitors in terms of Inverted Generational Distance and Hypervolume metrics. The results reveal that MRKP can quickly adapt to changing environments and provide good tracking ability when dealing with dynamic multiobjective optimization problems.

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Acknowledgements

The authors wish to thank the support of the National Key Research and Development Program of China under Grant (Grant No. 2018YFB1702300). the Youth Fund Natural Science Foundation of Hebei (No. E2018203162). the Project Supported by Natural Science Foundation-Steel and Iron Foundation of Hebei Province under Grant (No. E2019105123). and professor Yang jingming’s funding and supervision of the laboratory.

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Authors and Affiliations

  1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Lixin Wei, Zeyin Guo, Rui Fan & Hao Sun

  2. Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Lixin Wei, Zeyin Guo, Rui Fan & Hao Sun

  3. Department of Computer Science and Technology, Tangshan University, Hebei, 063000, China

    Zhiwei Zhao

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  1. Lixin Wei
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  2. Zeyin Guo
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Correspondence to Lixin Wei.

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Wei, L., Guo, Z., Fan, R. et al. A prediction strategy based on special points and multiregion knee points for evolutionary dynamic multiobjective optimization. Appl Intell 50, 4357–4377 (2020). https://doi.org/10.1007/s10489-020-01772-7

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  • DOI: https://doi.org/10.1007/s10489-020-01772-7

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