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.

In this report, we briefly describe our entry to the 2023 ISR competition: Planning Algorithms for Reconfiguring Independent Sets (PARIS 2023). Our solver is a modified version of the 2022 competition submission, which performed extremely well across several of the tracks Soh et al. [2022]. We have adapted the solver given the newly imposed resource limits and implemented a mechanism for the portfolio approach to return the best solution found during the resource limits. We additionally employ a suite of anytime search methods, which may produce better solutions. Careful handling of the time-limits was required to ensure that the solver responds with an answer in time. In the following, we describe the components of our planner and how we combine them for the different tracks.