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An iterated multi-stage selection hyper-heuristic

Kheiri, Ahmed; Özcan, Ender

Authors

Ahmed Kheiri



Abstract

There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach.

Citation

Kheiri, A., & Özcan, E. (2016). An iterated multi-stage selection hyper-heuristic. European Journal of Operational Research, 250(1), https://doi.org/10.1016/j.ejor.2015.09.003

Journal Article Type Article
Acceptance Date Sep 3, 2015
Online Publication Date Sep 10, 2015
Publication Date Apr 1, 2016
Deposit Date Mar 9, 2016
Publicly Available Date Mar 9, 2016
Journal European Journal of Operational Research
Print ISSN 0377-2217
Electronic ISSN 0377-2217
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 250
Issue 1
DOI https://doi.org/10.1016/j.ejor.2015.09.003
Keywords Heuristics, Combinatorial Optimisation, Hyper-heuristic,
Meta-heuristic, Hybrid Approach
Public URL http://eprints.nottingham.ac.uk/id/eprint/30192
Publisher URL http://www.sciencedirect.com/science/article/pii/S0377221715008255
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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