A learning automata based multiobjective hyper-heuristic

Li, Wenwen, Özcan, Ender and John, Robert (2017) A learning automata based multiobjective hyper-heuristic. IEEE Transactions on Evolutionary Computation . ISSN 1089-778X

Full text not available from this repository.


Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimisation 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 study 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 behaviour of two variants of the proposed selection hyper-heuristic, each utilising a different initialisation scheme are investigated across a range of unconstrained multiobjective mathematical benchmark functions from two different sets and the realworld problem of vehicle crashworthiness. The empirical results illustrate the effectiveness of our approach for cross-domain search, regardless of the initialisation scheme, on those problems when compared to each individual multiobjective algorithm. Moreover, both variants perform signicantly better than some previously proposed selection hyper-heuristics for multiobjective optimisation, thus signicantly enhancing the opportunities for improved multiobjective optimisation.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/901003
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.
Keywords: Online learning, Multiobjective optimisation, Hyper-heuristics, Evolutionary algorithms, Operational research
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1109/TEVC.2017.2785346
Depositing User: Eprints, Support
Date Deposited: 12 Dec 2017 14:13
Last Modified: 04 May 2020 19:23
URI: https://eprints.nottingham.ac.uk/id/eprint/48692

Actions (Archive Staff Only)

Edit View Edit View