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Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization

Author

Listed:
  • Yong Wang

    (School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China)

  • Kuichao Li

    (School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China)

  • Gai-Ge Wang

    (School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China)

Abstract

Dynamic multi-objective optimization problems (DMOPs) have been of interest to many researchers. These are problems in which the environment changes during the evolutionary process, such as the Pareto-optimal set (POS) or the Pareto-optimal front (POF). This kind of problem imposes more challenges and difficulties for evolutionary algorithms, mainly because it demands population to track the changing POF efficiently and accurately. In this paper, we propose a new approach combining key-points-based transfer learning and hybrid prediction strategies (KPTHP). In particular, the transfer process combines predictive strategy with obtaining anticipated key points depending on the previous moments to acquire the optimal individuals at the new instance during the evolution. Additionally, center-point-based prediction is used to complement transfer learning to comprehensively generate initial populations. KPTHP and six state-of-the-art algorithms are tested on various test functions for MIGD, DMIGD, MMS, and HVD metrics. KPTHP obtains superior results on most of the tested functions, which shows that our algorithm performs excellently in both convergence and diversity, with more competitiveness in addressing dynamic problems.

Suggested Citation

  • Yong Wang & Kuichao Li & Gai-Ge Wang, 2022. "Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization," Mathematics, MDPI, vol. 10(12), pages 1-34, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2117-:d:841644
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    References listed on IDEAS

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    1. Hossain, S. M. Zakir & Sultana, Nahid & Razzak, Shaikh A. & Hossain, Mohammad M., 2022. "Modeling and multi-objective optimization of microalgae biomass production and CO2 biofixation using hybrid intelligence approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
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    Cited by:

    1. Saddam Aziz & Cheung-Ming Lai & Ka Hong Loo, 2023. "Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application," Mathematics, MDPI, vol. 11(13), pages 1-18, July.
    2. Lining Xing & Rui Wu & Jiaxing Chen & Jun Li, 2022. "Handling Irregular Many-Objective Optimization Problems via Performing Local Searches on External Archives," Mathematics, MDPI, vol. 11(1), pages 1-19, December.

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