An iterated multi-stage selection hyper-heuristic

Kheiri, Ahmed ORCID: https://orcid.org/0000-0002-6716-2130 and Özcan, Ender 2016. An iterated multi-stage selection hyper-heuristic. European Journal of Operational Research 250 (1) , pp. 77-90. 10.1016/j.ejor.2015.09.003

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Uncontrolled Keywords:	Heuristics; Combinatorial optimisation; Hyper-heuristic; Meta-heuristic; Hybrid approach
Publisher:	Elsevier
ISSN:	0377-2217
Date of First Compliant Deposit:	17 October 2016
Date of Acceptance:	3 September 2015
Last Modified:	07 Nov 2023 05:37
URI:	https://orca.cardiff.ac.uk/id/eprint/85714

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