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Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO)

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Abstract

We present a family of local-search-based heuristics for Quadratic Unconstrained Binary Optimization (QUBO), all of which start with a (possibly fractional) initial point, sequentially improving its quality by rounding or switching the value of one variable, until arriving to a local optimum. The effects of various parameters on the efficiency of these methods are analyzed through computational experiments carried out on thousands of randomly generated problems having 20 to 2500 variables. Tested on numerous benchmark problems, the performance of the most competitive variant (ACSIOM) was shown to compare favorably with that of other published procedures.

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  1. RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ, 08854-8003, USA

    Endre Boros, Peter L. Hammer & Gabriel Tavares

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  1. Endre Boros
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  2. Peter L. Hammer
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  3. Gabriel Tavares
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Correspondence to Gabriel Tavares.

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G. Tavares supported in part by the Portuguese FCT and by the FSE in the context of the III Quadro Comunitário de Apoio.

P. L. Hammer, Our co-author and friend, Dr. Peter L. Hammer died in a tragic car accident while the final version of this paper was being prepared.

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Boros, E., Hammer, P.L. & Tavares, G. Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO). J Heuristics 13, 99–132 (2007). https://doi.org/10.1007/s10732-007-9009-3

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