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Hybrid Optimization pp 271–303Cite as

Constraint Programming and Local Search Hybrids

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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 45))

Abstract

Constraint programming and local search are two different optimization paradigms which, over the last two decades or so, have been successfully combined to form hybrid optimization techniques. This chapter describes and compares a number of these works, with the goal of giving a clear picture of research in this domain.We close with some open topics for the future.

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Notes

  1. 1.

    These techniques (min-conflicts included) are often geared toward the solution of decision problems, but optimization problems can be solved in the usual manner, through a series of such decision problems with a tightening upper bound on the objective function.

  2. 2.

    Later, GENET was generalized to Guided Local Search [110], a meta-heuristic based on penalization of the cost function.

  3. 3.

    Not all neighbors have to be available before exploration can start, but can be generated on the fly to keep memory consumption low.

  4. 4.

    Meta-heuristics such as simulated annealing or tabu search can be used by changing IloImprove to IloSimulatedAnnealing or IloTabuSearch.

  5. 5.

    This is important, as here we can draw on a wealth of knowledge on general-purpose and problem-dependent branching heuristics.

  6. 6.

    An error appeared in the original paper which indicated the opposite result for V s(x, y).

  7. 7.

    Setting d = ∞ does not eradicate randomness from the algorithm, only for one part of it.

  8. 8.

    If t 1 already contained G, then the dominance detection would not be possible using t 1.

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    Paul Shaw

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  1. , Department of Computer Science, Brown University, Providence, 02912, Rhode Island, USA

    Pascal van Hentenryck

  2. , Department of Electronics, Computer Scie, Università di Bologna, Viale Risorgimento 2, Bologna, 40136, Italy

    Michela Milano

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Shaw, P. (2011). Constraint Programming and Local Search Hybrids. In: van Hentenryck, P., Milano, M. (eds) Hybrid Optimization. Springer Optimization and Its Applications, vol 45. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1644-0_8

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