Feasibility restoration for iterative meta-heuristics search algorithms

Marcus Randall*

*Corresponding author for this work

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

2 Citations (Scopus)
159 Downloads (Pure)

Abstract

Many combinatorial optimisation problems have constraints that are difficult for meta-heuristic search algorithms to process. One approach is that of feasibility restoration. This technique allows the feasibility of the constraints of a problem to be broken and then brought back to a feasible state. The advantage of this is that the search can proceed over infeasible regions, thus potentially exploring difficult to reach parts of the state space. In this paper, a generic feasibility restoration scheme is proposed for use with the neighbourhood search algorithm simulated annealing. Some improved solutions to standard test problems are recorded.

Original languageEnglish
Title of host publicationDevelopments in Applied Artificial Intelligence - 15th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2002, Proceedings
EditorsTim Hendtlass, Moonis Ali
PublisherSpringer-Verlag London Ltd.
Pages168-178
Number of pages11
ISBN (Print)3540437819, 9783540437819
DOIs
Publication statusPublished - 1 Jan 2002
Event15th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2002 - Cairns, Cairns, Australia
Duration: 17 Jun 200220 Jun 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2358
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2002
CountryAustralia
CityCairns
Period17/06/0220/06/02

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  • Cite this

    Randall, M. (2002). Feasibility restoration for iterative meta-heuristics search algorithms. In T. Hendtlass, & M. Ali (Eds.), Developments in Applied Artificial Intelligence - 15th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2002, Proceedings (pp. 168-178). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2358). Springer-Verlag London Ltd.. https://doi.org/10.1007/3-540-48035-8_17