Local search enabled extremal optimisation for continuous inseparable multi-objective benchmark and real-world problems

Marcus Randall*, Andrew Lewis, Jan Hettenhausen, Timoleon Kipouros

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)
126 Downloads (Pure)

Abstract

Local search is an integral part of many meta-heuristic strategies that solve single objective optimisation problems. Essentially, the meta-heuristic is responsible for generating a good starting point from which a greedy local search will find the local optimum. Indeed, the best known solutions to many hard problems (such as the travelling salesman problem) have been generated in this hybrid way. However, for multiple objective problems, explicit local search strategies are relatively under studied, compared to other aspects of the search process. In this paper, a generic local search strategy is developed, particularly for problems where it is difficult or impossible to determine the contribution of individual solution components (often referred to as inseparable problems). The meta-heuristic adopted to test this is extremal optimisation, though the local search technique may be used by any meta-heuristic. To supplement the local search strategy a diversification strategy that draws from the external archive is incorporated into the local search strategy. Using benchmark problems, and a real-world airfoil design problem, it is shown that this combination leads to improved solutions.

Original languageEnglish
Title of host publication2014 International conference on computational science
EditorsD Abramson, M Lees, VV Krzhizhanovskaya, J Dongarra, PMA Sloot
Place of PublicationCairns
PublisherElsevier
Pages1904-1914
Number of pages11
Volume29
DOIs
Publication statusPublished - 2014
Event14th Annual International Conference on Computational Science - Cairns, Cairns, Australia
Duration: 10 Jun 201412 Jun 2014

Publication series

NameProcedia Computer Science
PublisherELSEVIER SCIENCE BV
Volume29
ISSN (Print)1877-0509

Conference

Conference14th Annual International Conference on Computational Science
CountryAustralia
CityCairns
Period10/06/1412/06/14

Cite this

Randall, M., Lewis, A., Hettenhausen, J., & Kipouros, T. (2014). Local search enabled extremal optimisation for continuous inseparable multi-objective benchmark and real-world problems. In D. Abramson, M. Lees, VV. Krzhizhanovskaya, J. Dongarra, & PMA. Sloot (Eds.), 2014 International conference on computational science (Vol. 29, pp. 1904-1914). (Procedia Computer Science; Vol. 29). Cairns: Elsevier. https://doi.org/10.1016/j.procs.2014.05.175