Exploiting variable associations to configure efficient local search algorithms in large-scale binary integer programs
This work addresses efficiency challenges in optimization algorithms for operations research, though it is incremental as it builds on existing local search methods.
The paper tackled the trade-off between solution quality and computation time in local search algorithms for binary integer programs by extracting variable associations to identify promising flipping pairs, resulting in improved performance for large-scale set covering and partitioning problems.
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. The quality of locally optimal solutions typically improves if a larger neighborhood is used, while the computation time of searching the neighborhood increases exponentially. To overcome this, we extract variable associations from the instance to be solved in order to identify promising pairs of flipping variables in the neighborhood search. Based on this, we develop a 4-flip neighborhood local search algorithm that incorporates an efficient incremental evaluation of solutions and an adaptive control of penalty weights. Computational results show that the proposed method improves the performance of the local search algorithm for large-scale set covering and partitioning problems.