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Handling Irregular Many-Objective Optimization Problems via Performing Local Searches on External Archives

Author

Listed:
  • Lining Xing

    (School of Electronic Engineering, Xidian University, Xi’an 710071, China)

  • Rui Wu

    (Inner Mongolia Institute of Dynamical Machinery, Hohhot 010010, China)

  • Jiaxing Chen

    (Inner Mongolia Institute of Dynamical Machinery, Hohhot 010010, China)

  • Jun Li

    (School of Management, Hunan Institute of Engineering, Xiangtan 411104, China)

Abstract

Adaptive weight-vector adjustment has been explored to compensate for the weakness of the evolutionary many-objective algorithms based on decomposition in solving problems with irregular Pareto-optimal fronts. One essential issue is that the distribution of previously visited solutions likely mismatches the irregular Pareto-optimal front, and the weight vectors are misled towards inappropriate regions. The fact above motivated us to design a novel many-objective evolutionary algorithm by performing local searches on an external archive, namely, LSEA. Specifically, the LSEA contains a new selection mechanism without weight vectors to alleviate the adverse effects of inappropriate weight vectors, progressively improving both the convergence and diversity of the archive. The solutions in the archive also feed back the weight-vector adjustment. Moreover, the LSEA selects a solution with good diversity but relatively poor convergence from the archive and then perturbs the decision variables of the selected solution one by one to search for solutions with better diversity and convergence. At last, the LSEA is compared with five baseline algorithms in the context of 36 widely-used benchmarks with irregular Pareto-optimal fronts. The comparison results demonstrate the competitive performance of the LSEA, as it outperforms the five baselines on 22 benchmarks with respect to metric hypervolume.

Suggested Citation

  • Lining Xing & Rui Wu & Jiaxing Chen & Jun Li, 2022. "Handling Irregular Many-Objective Optimization Problems via Performing Local Searches on External Archives," Mathematics, MDPI, vol. 11(1), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:10-:d:1008999
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    References listed on IDEAS

    as
    1. Haojie Chen & Hai Huang & Xingquan Zuo & Xinchao Zhao, 2022. "Robustness Enhancement of Neural Networks via Architecture Search with Multi-Objective Evolutionary Optimization," Mathematics, MDPI, vol. 10(15), pages 1-15, August.
    2. Qingqing Liu & Caixia Cui & Qinqin Fan, 2022. "Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method," Mathematics, MDPI, vol. 10(5), pages 1-23, March.
    3. Yong Wang & Kuichao Li & Gai-Ge Wang, 2022. "Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization," Mathematics, MDPI, vol. 10(12), pages 1-34, June.
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