Choice function based hyper-heuristics for multi-objective optimization

�zcan, Ender

Abstract

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic.

Citation

Özcan, E. (2015). Choice function based hyper-heuristics for multi-objective optimization. Applied Soft Computing, 28, https://doi.org/10.1016/j.asoc.2014.12.012

• Journal Article Type: Article
• Publication Date: Mar 1, 2015
• Deposit Date: Jan 5, 2016
• Publicly Available Date: Jan 5, 2016
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 28
• DOI: https://doi.org/10.1016/j.asoc.2014.12.012
• Keywords: Hyper-heuristic; Metaheuristic; Great deluge; Late acceptance; Multi-objective optimization
• Public URL: https://nottingham-repository.worktribe.com/output/984950
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1568494614006449