Qian-Long Dang, Wei Xu, Yang-Fei Yuan. A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization. International Journal of Automation and Computing.
Citation: Qian-Long Dang, Wei Xu, Yang-Fei Yuan. A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization. International Journal of Automation and Computing.

A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization

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  • Author Bio:

    Qian-Long Dang received the B. Eng. degree in applied mathematics from Henan University of Technology, China in 2016. He is currently a Ph. D. degree candidate in School of Mathematics and Statistics, Xidian University, China. His research interests include computational intelligence, swarm intelligence, evolution algorithm, and their applications.E-mail: xidianqldang@163.com (Corresponding author) ORCID iD: 0000-0001-9295-1361

    Wei Xu received the B. Eng. degree in applied mathematics from Inner Mongolia University of Science and Technology, China in 2018. He is currently a master student in School of Mathematics and Statistics, Xidian University, China.His research interests include computational intelligence, swarm intelligence, evolution algorithm, and their applications.E-mail: xuwei18782149972@163.com

    Yang-Fei Yuan received the B. Eng. degree in applied mathematics from Inner Mongolia University of Science and Technology, China in 2018. He is currently a master student in School of Mathematics and Statistics, Xidian University, China.His research interests include computational intelligence, swarm intelligence, evolution algorithm, and their applications.E-mail: st123456yyf@163.com

  • Received Date: 2021-04-15
  • Accepted Date: 2021-08-19
  • Many isolation approaches, such as zoning search, have been proposed to preserve the diversity in the decision space of multimodal multi-objective optimization (MMO). However, these approaches allocate the same computing resources for subspaces with different difficulties and evolution states. In order to solve this issue, this paper proposes a dynamic resource allocation strategy (DRAS) with reinforcement learning for multimodal multi-objective optimization problems (MMOPs). In DRAS, relative contribution and improvement are utilized to define the aptitude of subspaces, which can capture the potentials of subspaces accurately. Moreover, the reinforcement learning method is used to dynamically allocate computing resources for each subspace. In addition, the proposed DRAS is applied to zoning searches. Experimental results demonstrate that DRAS can effectively assist zoning search in finding more and better distributed equivalent Pareto optimal solutions in the decision space.

     

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