Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set Programming

TL;DR

This paper introduces Large Neighborhood Prioritized Search (LNPS), a metaheuristic for combinatorial optimization in Answer Set Programming that iteratively improves solutions through destroy and search phases, enhancing ASP solving performance.

Contribution

The paper presents LNPS, a novel metaheuristic for ASP optimization that allows flexible neighborhood exploration without reliance on specific destroy operators.

Findings

LNPS significantly improves ASP optimization performance.

LNPS is competitive with adaptive large neighborhood search.

The implementation demonstrates practical effectiveness of LNPS.

Abstract

We propose Large Neighborhood Prioritized Search (LNPS) for solving combinatorial optimization problems in Answer Set Programming (ASP). LNPS is a metaheuristic that starts with an initial solution and then iteratively tries to find better solutions by alternately destroying and prioritized searching for a current solution. Due to the variability of neighborhoods, LNPS allows for flexible search without strongly depending on the destroy operators. We present an implementation of LNPS based on ASP. The resulting heulingo solver demonstrates that LNPS can significantly enhance the solving performance of ASP for optimization. Furthermore, we establish the competitiveness of our LNPS approach by empirically contrasting it to (adaptive) large neighborhood search.