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Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization Problem

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  • Gintaras Palubeckis

Abstract

This paper describes and experimentally compares five rather different multistart tabu search strategies for the unconstrained binary quadratic optimization problem: a random restart procedure, an application of a deterministic heuristic to specially constructed subproblems, an application of a randomized procedure to the full problem, a constructive procedure using tabu search adaptive memory, and an approach based on solving perturbed problems. In the solution improvement phase a modification of a standard tabu search implementation is used. A computational trick applied to this modification – mapping of the current solution to the zero vector – allowed to significantly reduce the time complexity of the search. Computational results are provided for the 25 largest problem instances from the OR-Library and, in addition, for the 18 randomly generated larger and more dense problems. For 9 instances from the OR-Library new best solutions were found. Copyright Kluwer Academic Publishers 2004

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  • Gintaras Palubeckis, 2004. "Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization Problem," Annals of Operations Research, Springer, vol. 131(1), pages 259-282, October.
    • Handle: RePEc:spr:annopr:v:131:y:2004:i:1:p:259-282:10.1023/b:anor.0000039522.58036.68
    DOI: 10.1023/B:ANOR.0000039522.58036.68
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    Cited by:

    1. Wu, Zhengtian & Jiang, Baoping & Karimi, Hamid Reza, 2020. "A logarithmic descent direction algorithm for the quadratic knapsack problem," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Ricardo N. Liang & Eduardo A. J. Anacleto & Cláudio N. Meneses, 2022. "Data structures for speeding up Tabu Search when solving sparse quadratic unconstrained binary optimization problems," Journal of Heuristics, Springer, vol. 28(4), pages 433-479, August.
    3. Yitian Qian & Shaohua Pan & Shujun Bi, 2023. "A matrix nonconvex relaxation approach to unconstrained binary polynomial programs," Computational Optimization and Applications, Springer, vol. 84(3), pages 875-919, April.
    4. Michele Samorani & Yang Wang & Yang Wang & Zhipeng Lv & Fred Glover, 2019. "Clustering-driven evolutionary algorithms: an application of path relinking to the quadratic unconstrained binary optimization problem," Journal of Heuristics, Springer, vol. 25(4), pages 629-642, October.
    5. Gary Kochenberger & Jin-Kao Hao & Fred Glover & Mark Lewis & Zhipeng Lü & Haibo Wang & Yang Wang, 2014. "The unconstrained binary quadratic programming problem: a survey," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 58-81, July.
    6. Mauri, Geraldo Regis & Lorena, Luiz Antonio Nogueira, 2012. "A column generation approach for the unconstrained binary quadratic programming problem," European Journal of Operational Research, Elsevier, vol. 217(1), pages 69-74.
    7. Wei Chen & Liansheng Zhang, 2010. "Global optimality conditions for quadratic 0-1 optimization problems," Journal of Global Optimization, Springer, vol. 46(2), pages 191-206, February.
    8. Lü, Zhipeng & Glover, Fred & Hao, Jin-Kao, 2010. "A hybrid metaheuristic approach to solving the UBQP problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1254-1262, December.
    9. Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.
    10. Oleksii Mostovyi & Oleg A. Prokopyev & Oleg V. Shylo, 2013. "On Maximum Speedup Ratio of Restart Algorithm Portfolios," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 222-229, May.
    11. Wang, Yang & Lü, Zhipeng & Glover, Fred & Hao, Jin-Kao, 2012. "Path relinking for unconstrained binary quadratic programming," European Journal of Operational Research, Elsevier, vol. 223(3), pages 595-604.
    12. Gili Rosenberg & Mohammad Vazifeh & Brad Woods & Eldad Haber, 2016. "Building an iterative heuristic solver for a quantum annealer," Computational Optimization and Applications, Springer, vol. 65(3), pages 845-869, December.

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