A generality analysis of multiobjective hyper-heuristics

Li, Wenwen; Özcan, Ender; Drake, John H.; Maashi, Mashael

Abstract

Selection hyper-heuristics have emerged as high level general-purpose search methodologies that mix and control a set of low-level (meta) heuristics. Previous empirical studies over a range of single objective optimisation problems have shown that the number and type of low-level (meta) heuristics used are influential to the performance of selection hyper-heuristics. In addition, move acceptance strategies play an important role and can significantly affect the overall performance of a hyper-heuristic. In this paper, we introduce an adapted variant of an existing learning automata based multiobjective hyper-heuristic from the literature. We investigate the performance and generality level of the proposed method, and another learning automata based selection hyper-heuristic, operating over a search space of multiobjective evolutionary algorithms (MOEAs) across two well-known multiobjective optimisation benchmarks. The experimental results demonstrate that, regardless of the number and type of low-level metaheuristics available, the learning automata based hyper-heuristics outperform each constituent MOEA individually, and an online learning and random choice selection hyper-heuristic from the literature. This performance and generality is shown to be consistent across a number of different move acceptance strategies.

Citation

Li, W., Özcan, E., Drake, J. H., & Maashi, M. (2023). A generality analysis of multiobjective hyper-heuristics. Information Sciences, 627, 34-51. https://doi.org/10.1016/j.ins.2023.01.047

• Journal Article Type: Article
• Acceptance Date: Jan 5, 2023
• Online Publication Date: Jan 24, 2023
• Publication Date: 2023-05
• Deposit Date: Jun 11, 2024
• Publicly Available Date: Jun 12, 2024
• Journal: Information Sciences
• Print ISSN: 0020-0255
• Electronic ISSN: 1872-6291
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 627
• Pages: 34-51
• DOI: https://doi.org/10.1016/j.ins.2023.01.047
• Public URL: https://nottingham-repository.worktribe.com/output/30153097
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0020025523000476?via%3Dihub