Athanor: Local Search over Abstract Constraint Specifications

TL;DR

Athanor introduces a local search solver that operates directly on high-level abstract constraint specifications in Essence, enabling automatic neighborhood generation and improved scalability for combinatorial optimization problems.

Contribution

It presents a novel local search approach that begins from high-level Essence specifications, avoiding low-level modeling and enhancing performance.

Findings

Strong empirical performance compared to existing methods

Effective neighborhood generation from abstract types

Scalability benefits from high-level search

Abstract

Local search is a common method for solving combinatorial optimisation problems. We focus on general-purpose local search solvers that accept as input a constraint model - a declarative description of a problem consisting of a set of decision variables under a set of constraints. Existing approaches typically take as input models written in solver-independent constraint modelling languages like MiniZinc. The Athanor solver we describe herein differs in that it begins from a specification of a problem in the abstract constraint specification language Essence, which allows problems to be described without commitment to low-level modelling decisions through its support for a rich set of abstract types. The advantage of proceeding from Essence is that the structure apparent in a concise, abstract specification of a problem can be exploited to generate high quality neighbourhoods automatically, avoiding the difficult task of identifying that structure in an equivalent constraint model. Based on the twin benefits of neighbourhoods derived from high level types and the scalability derived by searching directly over those types, our empirical results demonstrate strong performance in practice relative to existing solution methods.