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A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition

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Listed:
  • Yuchao Su
  • Qiuzhen Lin
  • Jia Wang
  • Jianqiang Li
  • Jianyong Chen
  • Zhong Ming

Abstract

This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are generated closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems.

Suggested Citation

  • Yuchao Su & Qiuzhen Lin & Jia Wang & Jianqiang Li & Jianyong Chen & Zhong Ming, 2019. "A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition," Complexity, Hindawi, vol. 2019, pages 1-11, May.
  • Handle: RePEc:hin:complx:3251349
    DOI: 10.1155/2019/3251349
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    References listed on IDEAS

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    1. Xin Li & Jingang Lai & Ruoli Tang, 2017. "A Hybrid Constraints Handling Strategy for Multiconstrained Multiobjective Optimization Problem of Microgrid Economical/Environmental Dispatch," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    2. Xiaoyang Li & Deyun Zhou & Qian Pan & Yongchuan Tang & Jichuan Huang, 2018. "Weapon-Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition," Complexity, Hindawi, vol. 2018, pages 1-19, October.
    3. Qiuzhen Lin & Xiaozhou Wang & Bishan Hu & Lijia Ma & Fei Chen & Jianqiang Li & Carlos A. Coello Coello, 2018. "Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation," Complexity, Hindawi, vol. 2018, pages 1-18, November.
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