Adaptive multi-operator metaheuristics for quadratic assignment problems

M.M. Drugan, E.Gh. Talbi

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

5 Citations (Scopus)
1 Downloads (Pure)

Abstract

Local search based algorithms are a general and computational efficient metaheuristic. Restarting strategies are used in order to not be stuck in a local optimum. Iterated local search restarts the local search using perturbator operators, and the variable neighbourhood search alternates local search with various neighbourhoods. These two popular restarting techniques, or operators, evolve independently and are disconnected. We propose a metaheuristic framework, we call it multi-operator metaheuristics, which allows the alternative or simultaneously usage of the two restarting methods. Tuning the parameters, i.e. the neighbourhood size and the perturbation rate, is essential for the performance of metaheuristics. We automatically adapt the parameters for the two restarting operators using variants of adaptive pursuit for the multi-operators metaheuristic algorithms. We experimentally study the performance of several instances of the new class of metaheuristics on the quadratic assignment problem (QAP) instances, a wellknown and difficult combinatorial optimization problem.

Original languageEnglish
Title of host publicationEVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V
EditorsA.-A. Tantar, E. Tantar, J.-Q. Sun, W. Zhang, Q. Ding, O. Schütze, M. Emmerich, P. Legrand, P. Del Moral, C.A. Coello Coello
Place of PublicationBerlin
PublisherSpringer
Pages149-163
Number of pages15
ISBN (Print)9783319074931
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Evolutionary Computation V (EVOLVE 2014) - Beijing, China
Duration: 1 Jul 20144 Jul 2014
Conference number: V

Publication series

NameAdvances in Intelligent Systems and Computing
Volume288
ISSN (Print)21945357

Conference

ConferenceInternational Conference on Evolutionary Computation V (EVOLVE 2014)
Abbreviated titleEVOLVE 2014
Country/TerritoryChina
CityBeijing
Period1/07/144/07/14

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