Cemre CubukcuogluM. Fatih TasgetirenI. Sevil SariyildizLiang GaoMurat KucukvarTasgetiren, M. FatihKucukvar, MuratSariyildiz, I. SevilFatih Tasgetiren, M.Cubukcuoglu, CemreSevil Sariyildiz, I.Gao, LiangCH DagliGA Suer2025-10-0620192351-978910.1016/j.promfg.2020.01.3482-s2.0-85082736327http://dx.doi.org/10.1016/j.promfg.2020.01.348https://gcris.yasar.edu.tr/handle/123456789/7592https://doi.org/10.1016/j.promfg.2020.01.348Recently 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. (C) 2019 The Authors. Published by Elsevier Ltd.Englishinfo:eu-repo/semantics/openAccessmulti-objective quadratic assignment problems, metaheuristics, memetic algorithm, local search, genetic algorithmBIOBJECTIVE QAP, LAYOUTGenetic AlgorithmMulti-Objective Quadratic Assignment ProblemsMetaheuristicsMemetic AlgorithmLocal SearchConference Object