A Learning Automata-Based Multiobjective Hyper-Heuristic

Li, Wenwen; �zcan, Ender; John, Robert

Abstract

© 1997-2012 IEEE. Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimization problems. However, once implemented, their application to a new problem domain or a slight change in the problem description would often require additional expert intervention. There is a growing number of studies on reusable cross-domain search methodologies, such as selection hyper-heuristics, which are applicable to problem instances from various domains, requiring minimal expert intervention or even none. This paper introduces a new learning automata-based selection hyper-heuristic controlling a set of multiobjective metaheuristics. The approach operates above three well-known multiobjective evolutionary algorithms and mixes them, exploiting the strengths of each algorithm. The performance and behavior of two variants of the proposed selection hyper-heuristic, each utilizing a different initialization scheme are investigated across a range of unconstrained multiobjective mathematical benchmark functions from two different sets and the real-world problem of vehicle crashworthiness. The empirical results illustrate the effectiveness of our approach for cross-domain search, regardless of the initialization scheme, on those problems when compared to each individual multiobjective algorithm. Moreover, both variants perform significantly better than some previously proposed selection hyper-heuristics for multiobjective optimization, thus significantly enhancing the opportunities for improved multiobjective optimization.

Citation

Li, W., Özcan, E., & John, R. (2019). A Learning Automata-Based Multiobjective Hyper-Heuristic. IEEE Transactions on Evolutionary Computation, 23(1), 59-73. https://doi.org/10.1109/TEVC.2017.2785346

• Journal Article Type: Article
• Acceptance Date: Dec 11, 2017
• Online Publication Date: Dec 20, 2017
• Publication Date: Feb 1, 2019
• Deposit Date: Dec 12, 2017
• Publicly Available Date: Dec 20, 2017
• Journal: IEEE Transactions on Evolutionary Computation
• Print ISSN: 1089-778X
• Electronic ISSN: 1941-0026
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 1
• Pages: 59-73
• DOI: https://doi.org/10.1109/TEVC.2017.2785346
• Keywords: Online learning, Multiobjective optimisation,; Hyper-heuristics, Evolutionary algorithms, Operational research
• Public URL: https://nottingham-repository.worktribe.com/output/901003
• Publisher URL: http://ieeexplore.ieee.org/document/8231198/
• Additional Information: (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Dec 12, 2017