A multi-objective hyper-heuristic based on choice function

Maashi, Mashael; �zcan, Ender; Kendall, Graham

Abstract

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.

Citation

Maashi, M., Özcan, E., & Kendall, G. (2014). A multi-objective hyper-heuristic based on choice function. Expert Systems with Applications, 41(9), https://doi.org/10.1016/j.eswa.2013.12.050

• Journal Article Type: Article
• Publication Date: Jul 1, 2014
• Deposit Date: Mar 10, 2016
• Publicly Available Date: Mar 10, 2016
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Electronic ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 41
• Issue: 9
• DOI: https://doi.org/10.1016/j.eswa.2013.12.050
• Keywords: Hyper-heuristic; Metaheuristic; Evolutionary algorithm; Multi-objective optimization
• Public URL: https://nottingham-repository.worktribe.com/output/995348
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S095741741400013X