ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search

TL;DR

ParBalans enhances the parallel exploration capabilities of the Balans adaptive large neighborhood search for MIPs, leveraging multi-level parallelism to improve solution times on complex instances, showing competitive results against Gurobi.

Contribution

This paper introduces ParBalans, a parallel extension of Balans, enabling multi-level parallelism to improve MIP solving efficiency and scalability.

Findings

ParBalans achieves competitive performance on hard MIP benchmarks.

Parallel exploration significantly reduces solution times.

The approach scales well with increased computational resources.

Abstract

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.