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Hybrid neighborhood and global replacement strategies for multi objective evolutionary algorithm based on decomposition

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Abstract

In multi-objective evolutionary algorithm based on decomposition, replacement strategy (RS) plays a key role in balancing population convergence and diversity. However, the existing RSs either focus on the neighborhood or global replacement strategy, which may have difficulties in solving the complex multi-objective optimization problems. To solve this problem, a hybrid neighborhood and global replacement strategies for multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed. In this mechanism, a probability threshold pt is used to determine whether to implement neighborhood or global replacement strategy to balance convergence and diversity. Meanwhile, an offspring generation method is designed to generate high-quality offspring solutions for each sub-problem, which can improve the success of the replacement strategy. Based on MOEA/D, a new MOEA/D-HRS algorithm is designed, and it is compared with some state-of-the-art multi-objective evolutionary algorithms (MOEAs) in a series of bi-objective and three-objective test instances with linear or nonlinear various linkages. The experimental results show that MOEA/D-HRS can achieve the best performance in most test instances.

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Acknowledgment

This research is supported in part by the Anhui development and Reform Commission new energy vehicle and intelligent network vehicle industry technology innovation project, Project Name: Development and test of key parts of hydrogen fuel cell vehicle.

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Authors and Affiliations

  1. Information Engineering Department, Xingtai Polytechnic College, Xingtai, 054000, China

    Xiaoji Chen & Haibin Wang

  2. Chery Automobile Co., Ltd, Wuhu, 241006, China

    Xiaoji Chen, Bin Hai & Zhichao Wang

  3. Science and Technology Development Center, Xingtai Polytechnic College, Xingtai, 054000, China

    Jiankun Chu

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  1. Xiaoji Chen
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  2. Haibin Wang
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  3. Jiankun Chu
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  5. Zhichao Wang
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Correspondence to Xiaoji Chen.

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Chen, X., Wang, H., Chu, J. et al. Hybrid neighborhood and global replacement strategies for multi objective evolutionary algorithm based on decomposition. Evol. Intel. 15, 1715–1728 (2022). https://doi.org/10.1007/s12065-021-00582-1

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  • DOI: https://doi.org/10.1007/s12065-021-00582-1

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