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A two stages prediction strategy for evolutionary dynamic multi-objective optimization

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Abstract

In many engineering and scientific research processes, the dynamic multi-objective problems (DMOPs) are widely involved. It’s a quite challenge, which involves multiple conflicting objects changing over time or environment. The main task of DMOPs is tracking the Pareto front as soon as possible when the object changes over time. To accelerate the tracking process, a two stages prediction strategy (SPS) for DMOPs is proposed. To improve the prediction accuracy, population prediction is divided into center point prediction and manifold prediction when the change is detected. Due to the limitations of the support vector machine, the new population is predicted by the combination of the elite solution in the previous environment and Kalman filter in the early stage. Experimental results show that the proposed algorithm performs better on convergence and distribution when dealing with nonlinear problems, especially in the problems where the environmental change occurs frequently.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [No. 62003296, 61703361]; the Natural Science Foundation of Hebei [No. E2018203162, F20202 03031]; the Science and Technology Research Projects of Hebei [No. QN2020225]; the Post-Doctoral Research Projects of Hebei [No. B2019003021]; the Hebei Province Graduate Innovation Funding Project [CXZZBS2022134]. The authors would like to thank the editor and anonymous reviewers for their helpful comments and suggestions to improve the quality of this paper.

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Authors and Affiliations

  1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, People’s Republic of China

    Hao Sun, Xuemin Ma, Ziyu Hu, Jingming Yang & Huihui Cui

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

    Hao Sun, Xuemin Ma, Ziyu Hu, Jingming Yang & Huihui Cui

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  1. Hao Sun
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  2. Xuemin Ma
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Correspondence to Ziyu Hu.

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Appendix

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In order to facilitate readers to view the paper, the glossary of abbreviations is presented in Table 8.

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Sun, H., Ma, X., Hu, Z. et al. A two stages prediction strategy for evolutionary dynamic multi-objective optimization. Appl Intell 53, 1115–1131 (2023). https://doi.org/10.1007/s10489-022-03353-2

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