用多臂老虎机并行优化整数规划求解器参数，提速明显。
ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search
- 基于多臂老虎机并行探索多种参数配置
- 在困难实例上性能媲美商业求解器Gurobi
- 适合需要快速求解复杂整数规划的场景
混合整数规划（MIP）问题因其组合性质通常需要大量计算资源。并行化已成为加速求解、提升可扩展性的关键策略。本文研究了Balans——一种基于多臂老虎机的自适应大邻域搜索方法在MIP求解中的并行能力。尽管Balans具有模块化架构，天然支持对多种参数配置的并行探索，但这一潜力尚未被充分挖掘。为此，我们提出ParBalans，通过融合求解器级和算法级并行性，在复杂难解的MIP实例上显著提升性能。实验表明，ParBalans在多个硬基准测试中表现优异，与当前最先进的商业求解器Gurobi相比具有竞争力。
原文摘要 · Abstract (English)
Solving Mixed-Integer Programming (MIP) problems often requires substantial computational resources due to their combinatorial nature. Parallelization has emerged as a critical strategy to accelerate solution times and enhance scalability to tackle large, complex instances. This paper investigates the parallelization capabilities of Balans, a recently proposed multi-armed bandits-based adaptive large neighborhood search for MIPs. While Balans's modular architecture inherently supports parallel exploration of diverse parameter configurations, this potential has not been thoroughly examined. To address this gap, we introduce ParBalans, an extension that leverages both solver-level and algorithmic-level parallelism to improve performance on challenging MIP instances. Our experimental results demonstrate that ParBalans exhibits competitive performance compared to the state-of-the-art commercial solver Gurobi, particularly on hard optimization benchmarks.
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