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A Novel Adaptive Bandit-Based Selection Hyper-Heuristic for Multiobjective Optimization
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DOI：10.1109/TSMC.2023.3299982.png)
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摘要
En 中文
Despite the success of meta-heuristics for many computationally hard optimization problems, the design of meta-heuristics is dependent on the problem domain and needs to be tailored by experts. In contrast, selection hyper-heuristics are emerging cross-domain search methodologies that perform the search over the space of a set of low-level heuristics. In this study, we introduce a novel selection hyper-heuristic to learn, select, and apply low-level heuristics to solve multiobjective optimization problems. The proposed selection hyper-heuristic is an iterative process that includes three main components: 1) the reward balance strategy; 2) the bandit-based learning strategy; and 3) the multistage selection strategy. The performance and behavior of the proposed selection hyper-heuristic are investigated on a range of benchmarks as well as several real-world problems. The empirical results demonstrate the effectiveness and the cross-domain ability of the proposed selection hyper-heuristic by comparing it with several state-of-the-art algorithms.
Keyword:
Bandit-based learning strategy
multiobjective optimization
multistage selection strategy
reward balance strategy
selection hyper-heuristics
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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引用论文
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