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AuthorCubukcuoglu C.
AuthorFatih Tasgetiren M.
AuthorSevil Sariyildiz I.
AuthorGao L.
AuthorKucukvar M.
Available date2022-05-31T19:01:28Z
Publication Date2019
Publication NameProcedia Manufacturing
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.promfg.2020.01.348
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082736327&doi=http://dx.doi.org/10.1016%2fj.promfg.2020.01.348&partnerID=40&md5=db35f855f481cfcec5a1732e6295f28d
URIhttp://hdl.handle.net/10576/31869
AbstractRecently, multi-objective evolutionary algorithms (MOEAs) have been extensively used to solve multi-objective optimization problems (MOPs) since they have the ability to approximate a set of non-dominated solutions in reasonable CPU times. In this paper, we consider the bi-objective quadratic assignment problem (bQAP), which is a variant of the classical QAP, which has been extensively investigated to solve several real-life problems. The bQAP can be defined as having many input flows with the same distances between the facilities, causing multiple cost functions that must be optimized simultaneously. In this study, we propose a memetic algorithm with effective local search and mutation operators to solve the bQAP. Local search is based on swap neighborhood structure whereas the mutation operator is based on ruin and recreate procedure. The experimental results show that our bi-objective memetic algorithm (BOMA) substantially outperforms all the island-based variants of the PASMOQAP algorithm proposed very recently in the literature.
Languageen
PublisherElsevier B.V.
SubjectGenetic algorithm
Local search
Memetic algorithm
Metaheuristics
Multi-objective quadratic assignment problems
TitleA memetic algorithm for the bi-objective quadratic assignment problem
TypeConference Paper
Pagination1215-1222
Volume Number39
dc.accessType Abstract Only


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