  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.
