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Adaptive Kernel Search: A heuristic for solving Mixed Integer linear Programs

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  • Guastaroba, G.
  • Savelsbergh, M.
  • Speranza, M.G.

Abstract

We introduce Adaptive Kernel Search (AKS), a heuristic framework for the solution of (general) Mixed Integer linear Programs (MIPs). AKS extends and enhances Kernel Search, a heuristic framework that has been shown to produce high-quality solutions for a number of specific (combinatorial) optimization problems in a short amount of time. AKS solves a sequence of carefully constructed restricted MIPs (using a commercial MIP solver). The computational effort required to solve the first restricted MIP guides the construction of the subsequent MIPs. The restricted MIPs are constructed around a kernel, which contains the variables that are presumably non-zero in an optimal solution. Computational results, for a set of 137 instances, show that AKS significantly outperforms other state-of-the-art heuristics for solving MIPs. AKS also compares favorably to CPLEX and offers more flexibility to trade-off solution quality and computing time.

Suggested Citation

  • Guastaroba, G. & Savelsbergh, M. & Speranza, M.G., 2017. "Adaptive Kernel Search: A heuristic for solving Mixed Integer linear Programs," European Journal of Operational Research, Elsevier, vol. 263(3), pages 789-804.
    • Handle: RePEc:eee:ejores:v:263:y:2017:i:3:p:789-804
    DOI: 10.1016/j.ejor.2017.06.005
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    References listed on IDEAS

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    1. Filippi, C. & Guastaroba, G. & Speranza, M.G., 2016. "A heuristic framework for the bi-objective enhanced index tracking problem," Omega, Elsevier, vol. 65(C), pages 122-137.
    2. Marianna De Santis & Stefano Lucidi & Francesco Rinaldi, 2013. "A new class of functions for measuring solution integrality in the Feasibility Pump approach: Complete Results," DIAG Technical Reports 2013-05, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
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    8. Enrico Angelelli & Renata Mansini & M. Speranza, 2012. "Kernel Search: a new heuristic framework for portfolio selection," Computational Optimization and Applications, Springer, vol. 51(1), pages 345-361, January.
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