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A multiobjective evolutionary algorithm based on surrogate individual selection mechanism

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Abstract

Recently, classification-based preselection (CPS) strategy for evolutionary multiobjective optimization has been found to be very effective and efficient for solving complicated multiobjective optimization problems (MOPs). However, this strategy can only classify the candidate solutions into different categories, but it is difficult to find out which one is the best. In order to overcome this shortcoming, we propose a surrogate individual selection mechanism for multiobjective evolutionary algorithm based on decomposition. In this mechanism, we get the best one from candidate solution set by surrogate model, which mitigates the risk of using CPS strategy. Furthermore, we generate candidate solution set through a new offspring generation strategy, which can improve the quality of the candidate solutions. Based on typical multiobjective evolutionary algorithm MOEA/D, we design a new algorithm framework, called MOEA/D-SISM, through integrating the proposed surrogate individual selection mechanism. We compare MOEA/D-SISM with other state-of-the-art multiobjective evolutionary algorithms (MOEAs), and experimental results show that our proposed algorithm obtains the best performance.

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Funding

This research is supported in part by National Key Research and Development Program “New Energy Vehicle” Key Special Project Subsidy, Project Name: “Research and Development of Electronic and Electrical Architecture of Intelligent Electric Vehicle”, Project No. 2017YFB0102500. Xingtai City Science and Technology Bureau, Project Name: Research on obstacle avoidance method of autonomous intelligent electric vehicle in complex environment, Project No.2018ZC022.

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Authors and Affiliations

  1. Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xiaoji Chen & Bin Wu

  2. School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China

    Pengcheng Sheng

  3. Automotive Engineering Department, Xingtai Polytechnic College, Xingtai, 054000, China

    Pengcheng Sheng

Authors
  1. Xiaoji Chen
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  2. Bin Wu
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  3. Pengcheng Sheng
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Correspondence to Bin Wu.

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Chen, X., Wu, B. & Sheng, P. A multiobjective evolutionary algorithm based on surrogate individual selection mechanism. Pers Ubiquit Comput 23, 421–434 (2019). https://doi.org/10.1007/s00779-019-01211-6

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  • DOI: https://doi.org/10.1007/s00779-019-01211-6

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