ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization
Zhihan Chen, Peng Lin, Hao Hu, Shaowei Cai
TL;DR
This work tackles solving Pseudo-Boolean Optimization with local search by introducing a dynamic scoring mechanism (DLS-PBO) that adaptively balances hard-constraint satisfaction and objective improvement. Building on this, the authors present ParLS-PBO, a parallel solver that uses a solution pool and polarity-density guidance to share high-quality solutions across worker threads and steer search. Empirical results show that ParLS-PBO achieves superior performance against several state-of-the-art solvers, and it scales effectively up to 32 threads, approaching the performance of parallel commercial solvers. The approach has potential applicability to related domains such as SAT and MaxSAT and suggests a path toward distributed, cloud-based PBO solving.
Abstract
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.