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Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization
IEEE/CAA Journal of Automatica Sinica ( IF 11.8 ) Pub Date : 2021-05-11 , DOI: 10.1109/jas.2021.1004027
Qinqin Fan , Okan K. Ersoy

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.

中文翻译:

平衡与不平衡多峰多目标优化的自适应资源分配分区搜索

维持种群多样性是多模式多目标优化中的重要任务。尽管分区搜索(ZS)可以改善决策空间中的多样性,但是当计算资源有限时,尤其是在不平衡问题上,将相同的计算成本分配给每个搜索子空间可能是浪费的。为了缓解上述问题,本研究提出了一种采用自适应资源分配(ZS-ARA)的分区搜索方法。在提出的ZS-ARA中,整个搜索空间被分为许多子空间,以保留决策空间的多样性并降低问题的复杂性。此外,可以在所有子空间之间自动分配计算资源。在两种不同类型的多峰多目标问题(MMOP)上,将ZS-ARA与七种算法进行了比较,即平衡和不平衡的MMOP。结果表明,与ZS相似,ZS-ARA在平衡MMOP的情况下也实现了高性能。而且,它可以极大地帮助“常规”算法提高不平衡MMOP的性能,并且能够动态分配有限的计算资源。
更新日期:2021-05-14
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