Transforming Constraint Programs to Input for Local Search

TL;DR

This paper introduces a method to automatically generate local search neighborhoods from constraint specifications by leveraging symmetry properties, simplifying the application of local search algorithms to combinatorial problems.

Contribution

It establishes a novel connection between symmetry in constraint problems and local search neighborhoods, enabling automatic neighborhood generation within the IDP system.

Findings

Automatically generated neighborhoods are viable for classical optimization problems.

Symmetry properties can be effectively used to inform local search strategies.

The technique reduces human intervention in preparing data for local search.

Abstract

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.