GPU Parallel Pareto Local Search for Multi-objective QUBO

Abstract

We revisit Pareto Local Search (PLS) for multi-objective quadratic unconstrained binary optimization (QUBO) problems. Multi-objective QUBO formulations have gained increasing attention as a universal modeling method and natural input for quantum optimization. While exact methods exist, their unconstrained binary structure makes them well-suited for heuristics such as PLS. At the same time, GPUs offer a powerful classical alternative to quantum-inspired optimization. We develop an efficient GPU-based parallel implementation of PLS for multi-objective QUBO problems. Our approach combines coarse-grained and fine-grained parallelization. On the coarse-grained level, an initial set of supported solutions is generated using weighted-sum optimization, from which multiple PLS processes are started in parallel. At the fine-grained level, neighborhood exploration is parallelized on the GPU by simultaneously evaluating binary-flip candidate solutions across all objectives. Non-dominated solutions are maintained using an ND-Tree data structure to support efficient Pareto updates. The implementation combines several known PLS techniques within a unified CPU/GPU hybrid architecture. We describe the algorithmic components, the GPU-specific design decisions, and report results from a computational study evaluating the effectiveness of the proposed approach.