Transforming Constraint Programs to Input for Local Search
For practitioners using constraint programming, this work reduces the need for manual intervention when applying local search to combinatorial optimization problems.
The paper establishes a link between symmetry properties of constraint optimization problems and local search neighborhoods, enabling automatic generation of neighborhoods from constraint specifications. Evaluation on six classical problems shows the technique is viable.
Applying local search algorithms to combinatorial optimization problems is not an easy feat. Typically, human intervention is required to compile the constraints to input data for some metaheuristic algorithm. In this paper, we establish a link between symmetry properties of constraint optimization problems and local search neighborhoods, and we use this link to automatically generate neighborhoods from a constraint specification in the context of the IDP system. We evaluate the obtained neighborhoods for six classical optimization problems. The resulting observations support the viability of this technique.