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Volume 8 Issue 6
Jun.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(6): 1163-1176
Q. Q. Fan, Okan K. Ersoy, "Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1163-1176, Jun. 2021. doi: 10.1109/JAS.2021.1004027
Citation: Q. Q. Fan, Okan K. Ersoy, "Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1163-1176, Jun. 2021. doi: 10.1109/JAS.2021.1004027
shu

Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization

doi: 10.1109/JAS.2021.1004027
  • 1.

    Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China

  • 2.

    School of Electronic and Computer Engineering, Purdue University, West Lafayette, IN 47906 USA

Funds:  This work was partially supported by the Shandong Joint Fund of the National Nature Science Foundation of China (U2006228), and the National Nature Science Foundation of China (61603244)
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    Abstract
  • Maintaining population diversity is an important task in the multimodal multi-objective optimization. Although the zoning search (ZS) can improve the diversity in the decision space, assigning the same computational costs to each search subspace may be wasteful when computational resources are limited, especially on imbalanced problems. To alleviate the above-mentioned issue, a zoning search with adaptive resource allocating (ZS-ARA) method is proposed in the current study. In the proposed ZS-ARA, the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity. Moreover, the computational resources can be automatically allocated among all the subspaces. The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems (MMOPs), namely, balanced and imbalanced MMOPs. The results indicate that, similarly to the ZS, the ZS-ARA achieves high performance with the balanced MMOPs. Also, it can greatly assist a “regular” algorithm in improving its performance on the imbalanced MMOPs, and is capable of allocating the limited computational resources dynamically.

     

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    Highlights

    • The ZS-SRA can allocate different computational resources among all subspaces
    • The ZS-SRA can assist “regular” MMOEA in finding more equivalent solutions on imbalanced MMOPs
    • The ZS-SRA outperforms “special” and “regular” MMOEAs on imbalanced and balanced MMOPs

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