ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization

TL;DR

This paper introduces ParLS-PBO, a parallel local search solver for Pseudo-Boolean Optimization that improves upon existing methods by incorporating dynamic scoring, solution sharing, and diversity considerations, achieving competitive performance.

Contribution

It presents the first parallel local search solver for PBO, enhancing LSPBO with dynamic scoring and solution sharing mechanisms for improved optimization performance.

Findings

Parallel approach outperforms sequential methods

Competitive with Gurobi in experiments

Effective solution sharing improves search quality

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.