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A cooperative parallel tabu search algorithm for the quadratic assignment problem

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  • James, Tabitha
  • Rego, Cesar
  • Glover, Fred

Abstract

In this study, we introduce a cooperative parallel tabu search algorithm (CPTS) for the quadratic assignment problem (QAP). The QAP is an NP-hard combinatorial optimization problem that is widely acknowledged to be computationally demanding. These characteristics make the QAP an ideal candidate for parallel solution techniques. CPTS is a cooperative parallel algorithm in which the processors exchange information throughout the run of the algorithm as opposed to independent concurrent search strategies that aggregate data only at the end of execution. CPTS accomplishes this cooperation by maintaining a global reference set which uses the information exchange to promote both intensification and strategic diversification in a parallel environment. This study demonstrates the benefits that may be obtained from parallel computing in terms of solution quality, computational time and algorithmic flexibility. A set of 41 test problems obtained from QAPLIB were used to analyze the quality of the CPTS algorithm. Additionally, we report results for 60 difficult new test instances. The CPTS algorithm is shown to provide good solution quality for all problems in acceptable computational times. Out of the 41 test instances obtained from QAPLIB, CPTS is shown to meet or exceed the average solution quality of many of the best sequential and parallel approaches from the literature on all but six problems, whereas no other leading method exhibits a performance that is superior to this.

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  • James, Tabitha & Rego, Cesar & Glover, Fred, 2009. "A cooperative parallel tabu search algorithm for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 195(3), pages 810-826, June.
    • Handle: RePEc:eee:ejores:v:195:y:2009:i:3:p:810-826
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    References listed on IDEAS

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    1. Loiola, Eliane Maria & de Abreu, Nair Maria Maia & Boaventura-Netto, Paulo Oswaldo & Hahn, Peter & Querido, Tania, 2007. "A survey for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 176(2), pages 657-690, January.
    2. Drezner, Zvi, 2005. "The extended concentric tabu for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 160(2), pages 416-422, January.
    3. Haghani, Ali & Chen, Min-Ching, 1998. "Optimizing gate assignments at airport terminals," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(6), pages 437-454, August.
    4. Zvi Drezner & Peter Hahn & Éeric Taillard, 2005. "Recent Advances for the Quadratic Assignment Problem with Special Emphasis on Instances that are Difficult for Meta-Heuristic Methods," Annals of Operations Research, Springer, vol. 139(1), pages 65-94, October.
    5. Zvi Drezner, 2003. "A New Genetic Algorithm for the Quadratic Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 15(3), pages 320-330, August.
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    Cited by:

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    2. Krešimir Mihić & Kevin Ryan & Alan Wood, 2018. "Randomized Decomposition Solver with the Quadratic Assignment Problem as a Case Study," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 295-308, May.
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    4. Silva, Allyson & Coelho, Leandro C. & Darvish, Maryam, 2021. "Quadratic assignment problem variants: A survey and an effective parallel memetic iterated tabu search," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1066-1084.
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    6. Paul, G., 2011. "An efficient implementation of the robust tabu search heuristic for sparse quadratic assignment problems," European Journal of Operational Research, Elsevier, vol. 209(3), pages 215-218, March.
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    10. Yeh, Cheng-Ta & Fiondella, Lance, 2017. "Optimal redundancy allocation to maximize multi-state computer network reliability subject to correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 138-150.
    11. Mario Inostroza-Ponta & Regina Berretta & Pablo Moscato, 2011. "QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-18, January.
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