Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set Programming
This is an incremental improvement for researchers and practitioners in combinatorial optimization using Answer Set Programming, offering a flexible search method that reduces dependency on destroy operators.
The paper tackles combinatorial optimization in Answer Set Programming by proposing Large Neighborhood Prioritized Search (LNPS), a metaheuristic that iteratively destroys and searches solutions, resulting in significantly enhanced solving performance as demonstrated by the heulingo solver.
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.