Artificial Bee Colony Optimization for the Quadratic Assignment Problem

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dc.contributor.author Dokeroglu, Tansel
dc.contributor.author Sevinc, Ender
dc.contributor.author Cosar, Ahmet
dc.date.accessioned 2019-06-28T12:35:13Z
dc.date.available 2019-06-28T12:35:13Z
dc.date.issued 2019
dc.identifier.issn 1568-4946
dc.identifier.issn 1872-9681
dc.identifier.uri https://doi.org/10.1016/j.asoc.2019.01.001
dc.identifier.uri http://hdl.handle.net/20.500.12485/348
dc.description.abstract We propose hybrid Artificial Bee Colony (ABC) optimization algorithms for the well-known Quadratic Assignment Problem (QAP). Large problem instances of the QAP are still very challenging. Scientists have not discovered any method to obtain the exact solutions for these difficult problems yet. The ABC has been reported to be an efficient meta-heuristic for the solution of many intractable problems. It has promising results making it a good candidate to obtain (near)-optimal solutions for well-known NP-Hard problems. The proposed ABC algorithm (ABC-QAP) and its parallel version (PABC-QAP) are the first applications of the ABC meta-heuristic together with Tabu search to the optimization of the QAP. The behavior of employed, onlooker and scout bees are modeled by using the distributed memory parallel computation paradigm for large problem instances of the QAP. Scout bees search for food sources, employed bees go to food source and return to hive and share their information on the dance area, onlooker bees watch the dance of employed bees and choose food sources depending on the dance. Robust Tabu search method is used to simulate exploration and exploitation processes of the bees. 125 of 134 benchmark problem instances are solved optimally from the QAPLIB library and 0.27% deviation is reported for 9 large problem instances that could not be solved optimally. The performance of the ABC optimization algorithms is competitive with state-of-the-art meta-heuristic algorithms in literature. (C) 2019 Elsevier B.V. All rights reserved. en_US
dc.language.iso en en_US
dc.subject Computer Science en_US
dc.title Artificial Bee Colony Optimization for the Quadratic Assignment Problem en_US
dc.type Article en_US
dc.relation.journal Applied Soft Computing
dc.identifier.startpage 595
dc.identifier.endpage 606
dc.identifier.volume 76

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