Exploiting variable associations to configure efficient local search algorithms in large-scale binary integer programs. (arXiv:1604.08448v1 [cs.DS])

We present a data mining approach for reducing the search space of local search algorithms in a class of binary integer programs including the set covering and partitioning problems. We construct a k-nearest neighbor graph by extracting variable associations from the instance to be solved, in order to identify promising pairs of flipping variables in the neighborhood search. We also develop a 4-flip neighborhood local search algorithm that flips four variables alternately along 4-paths or 4-cycles in the k-nearest neighbor graph. We incorporate an efficient incremental evaluation of solutions and an adaptive control of penalty weights into the 4-flip neighborhood local search algorithm. Computational comparison with the latest solvers shows that our algorithm performs effectively for large-scale set covering and partitioning problems.

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