ParLS-PBO：一种用于伪布尔优化的并行局部搜索求解器
ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization
AI总结：
本文提出 ParLS-PBO，通过动态评分机制改进 LSPBO，并基于可行解池共享与极性密度增强评分，实现首个并行局部搜索 PBO 求解器，实验性能可与 Gurobi 并行版竞争。
摘要：
作为众多优化问题中广泛应用的技术，局部搜索近来已被用于求解伪布尔优化（PBO）问题。LSPBO 是 PBO 的一个代表性局部搜索求解器。本文首先通过一种动态评分机制改进 LSPBO，该机制在硬约束得分与目标函数得分之间动态取得平衡。此外，在这一改进的 LSPBO 基础上，我们开发了第一个并行局部搜索 PBO 求解器。其主要思想是通过维护一个可行解池，在不同线程之间共享优质解以引导搜索。为了在更新解池时评估解，我们提出了一种同时考虑解质量和池多样性的函数。进一步地，我们计算池中的极性密度以增强局部搜索的评分函数。我们的实验表明，所提出的并行方法具有明显优势，使其能够与著名商业求解器 Gurobi 的并行版本相竞争。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
