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المؤلفCubukcuoglu C.
المؤلفFatih Tasgetiren M.
المؤلفSevil Sariyildiz I.
المؤلفGao L.
المؤلفKucukvar M.
تاريخ الإتاحة2022-05-31T19:01:28Z
تاريخ النشر2019
اسم المنشورProcedia Manufacturing
المصدرScopus
المعرّفhttp://dx.doi.org/10.1016/j.promfg.2020.01.348
معرّف المصادر الموحدhttps://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
معرّف المصادر الموحدhttp://hdl.handle.net/10576/31869
الملخصRecently, 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.
اللغةen
الناشرElsevier B.V.
الموضوعGenetic algorithm
Local search
Memetic algorithm
Metaheuristics
Multi-objective quadratic assignment problems
العنوانA memetic algorithm for the bi-objective quadratic assignment problem
النوعConference Paper
الصفحات1215-1222
رقم المجلد39
dc.accessType Abstract Only


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