A multi-objective hyper-heuristic based on choice function

Maashi, Mashael, Özcan, Ender and Kendall, Graham (2014) A multi-objective hyper-heuristic based on choice function. Expert Systems with Applications, 41 (9). pp. 4475-4493. ISSN 0957-4174

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Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/995348
Keywords: Hyper-heuristic; Metaheuristic; Evolutionary algorithm; Multi-objective optimization
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.eswa.2013.12.050
Depositing User: Ozcan, Dr Ender
Date Deposited: 10 Mar 2016 10:04
Last Modified: 04 May 2020 20:14
URI: https://eprints.nottingham.ac.uk/id/eprint/32175

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