ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization
This work addresses optimization problems in domains like scheduling and resource allocation by providing a competitive parallel solver, though it is incremental as it builds on existing local search methods.
The authors tackled the Pseudo-Boolean Optimization (PBO) problem by developing a parallel local search solver that improves upon LSPBO with a dynamic scoring mechanism and solution sharing among threads, achieving competitiveness with the parallel version of Gurobi in empirical tests.
As a broadly applied technique in numerous optimization problems, recently, local search has been employed to solve Pseudo-Boolean Optimization (PBO) problem. A representative local search solver for PBO is LSPBO. In this paper, firstly, we improve LSPBO by a dynamic scoring mechanism, which dynamically strikes a balance between score on hard constraints and score on the objective function. Moreover, on top of this improved LSPBO , we develop the first parallel local search PBO solver. The main idea is to share good solutions among different threads to guide the search, by maintaining a pool of feasible solutions. For evaluating solutions when updating the pool, we propose a function that considers both the solution quality and the diversity of the pool. Furthermore, we calculate the polarity density in the pool to enhance the scoring function of local search. Our empirical experiments show clear benefits of the proposed parallel approach, making it competitive with the parallel version of the famous commercial solver Gurobi.