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Adaptive population structure learning in evolutionary multi-objective optimization

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Abstract

Some recent research shows that in multi-objective evolutionary algorithms (MOEAs), mating with similar individuals can improve the quality of new solutions and accelerate the convergence of algorithms. Based on the above finding, some clustering-based mating restriction strategies are proposed. However, those clustering algorithms are not suitable for the population with non-convex structures. Therefore, it may fail to detect population structure in different evolutionary stages. To solve this problem, we propose a normalized hypervolume-based mating transformation strategy (NMTS). In NMTS, the population structure is detected by K-nearest-neighbor graph and spectral clustering before and after the mating transformation condition, respectively. And the parent solutions are chosen according to the founded population structure. The proposed algorithm has been applied to a number of test instances with complex Pareto optimal solution sets or Pareto fronts, and compared with some state-of-the-art MOEAs. The results have demonstrated its advantages over other algorithms.

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Funding

This study was found by National Natural Science Foundation of China (Grant numbers: 61703382, 51875053, 61673180) and China Ministry of Science and Technology Key Research and Development Program (Grant number: 2018YFC1903101).

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Authors and Affiliations

  1. School of Mechanical Engineering, Changzhou University, Changzhou, 213164, China

    Shuai Wang & Yi Zhang

  2. Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing Electro-mechanical Engineering Institute, Beijing, 100074, China

    Hu Zhang

  3. Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai, 200062, China

    Aimin Zhou

Authors
  1. Shuai Wang
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  2. Hu Zhang
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  3. Yi Zhang
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  4. Aimin Zhou
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Correspondence to Hu Zhang.

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Communicated by V. Loia.

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Wang, S., Zhang, H., Zhang, Y. et al. Adaptive population structure learning in evolutionary multi-objective optimization. Soft Comput 24, 10025–10042 (2020). https://doi.org/10.1007/s00500-019-04518-x

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