Abstract
Large neighbourhood search, a meta-heuristic, has proven to be successful on a wide range of optimisation problems. The algorithm repeatedly generates and searches through a neighbourhood around the current best solution. Thus, it finds increasingly better solutions by solving a series of simplified problems, all of which are related to the current best solution. In this paper, we show that significant benefits can be obtained by simulating local-search behaviour in constraint programming by using phase saving based on the best solution found so far during the search, activity-based search (VSIDS), and nogood learning. The approach is highly effective despite its simplicity, improving the highest scoring solver, Chuffed, in the free category of the MiniZinc Challenge 2017, and can be easily integrated into modern constraint programming solvers. We validated the results on a wide range of benchmarks from the competition library, comparing against seventeen state-of-the-art solvers.
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Acknowledgements
We would like to thank Andreas Schutt for his exceptional assistance with comparing solvers and Graeme Gange for his insight on the implementation.
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Demirović, E., Chu, G., Stuckey, P.J. (2018). Solution-Based Phase Saving for CP: A Value-Selection Heuristic to Simulate Local Search Behavior in Complete Solvers. In: Hooker, J. (eds) Principles and Practice of Constraint Programming. CP 2018. Lecture Notes in Computer Science(), vol 11008. Springer, Cham. https://doi.org/10.1007/978-3-319-98334-9_7
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DOI: https://doi.org/10.1007/978-3-319-98334-9_7
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