{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21890",
  "author_total": 2,
  "authors": [
    {
      "name": "Stephanie Riedmüller"
    },
    {
      "name": "Thorsten Koch"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8r82qjtqfkch0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21890",
  "fulltext_url": null,
  "id": "8r82qjtqfkch0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21890",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "1703|Advanced Multi-Objective Optimization Algorithms"
    ]
  ],
  "openalex": "W7222555161",
  "pdf_url": "https://arxiv.org/pdf/2610.21890",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8r82qjtqfkch0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:54Z",
      "native_id": "2610.21890",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21890"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "GPU Parallel Pareto Local Search for Multi-objective QUBO",
  "title_text": "GPU Parallel Pareto Local Search for Multi-objective QUBO",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:54Z",
  "url": "https://research.draconitic.com/paper/8r82qjtqfkch0",
  "venue": "arXiv",
  "versions": []
}