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Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization Problem

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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.

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  1. Department of Practical Informatics, Kaunas University of Technology, Studentu 50, 3031, Kaunas, Lithuania

    Gintaras Palubeckis

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  1. Gintaras Palubeckis
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Palubeckis, G. Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization Problem. Ann Oper Res 131, 259–282 (2004). https://doi.org/10.1023/B:ANOR.0000039522.58036.68

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