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    A memetic algorithm for the bi-objective quadratic assignment problem

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    Date
    2019
    Author
    Cubukcuoglu C.
    Fatih Tasgetiren M.
    Sevil Sariyildiz I.
    Gao L.
    Kucukvar M.
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    Abstract
    Recently, 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.
    URI
    https://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
    DOI/handle
    http://dx.doi.org/10.1016/j.promfg.2020.01.348
    http://hdl.handle.net/10576/31869
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    • Mechanical & Industrial Engineering [‎1460‎ items ]

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