Metraipolis › archive › dc_dcd1def77cd2

LLM-Driven Joint Evolution of Coupled Heuristics Components for Routing Optimization — arXiv:2609.02353

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02353
License (as archived): CC0-1.0
Archived: 2026-10-17T05:53:17.564Z
SHA-256 of the archived text: 843423fb01417fec0dbdfafef7162b6234b1c7199e76eaec1562092736879c88
Stable URL of this page: https://metraipolis.com/documento/dc_dcd1def77cd2

How to cite

LLM-Driven Joint Evolution of Coupled Heuristics Components for Routing Optimization — arXiv:2609.02353 — arXiv (en), https://arxiv.org/abs/2609.02353 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dcd1def77cd2 (Metraipolis archive dc_dcd1def77cd2, sha256 843423fb01417fec0dbdfafef7162b6234b1c7199e76eaec1562092736879c88)

BibTeX

@misc{dcdcd1def77cd2,
  title = {LLM-Driven Joint Evolution of Coupled Heuristics Components for Routing Optimization — arXiv:2609.02353},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02353},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dcd1def77cd2; sha256 843423fb01417fec0dbdfafef7162b6234b1c7199e76eaec1562092736879c88},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02353. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Heuristic design for combinatorial optimization remains heavily reliant on expert knowledge, while existing large language model (LLM)-enhanced evolutionary methods typically evolve isolated algorithmic components, even when one determines the search state on which another operates. This paper proposes LLM-driven Heuristic Components Joint Generation (LLM-HCJG), a population-based framework that jointly generates and co-evolves interdependent heuristic components under a shared design blueprint. Applied to guided local search (GLS), LLM-HCJG couples solution initialization with penalty construction and embeds the generated pair into an enhanced online search mechanism. The resulting form is further transferred from the traveling salesman problem (TSP) to the capacitated vehicle routing problem (CVRP). Theoretical analysis establishes the non-separable state-transition effects between the two components and the advantage in generation consistency. Across synthetic instances and 41 public TSPLIB/CVRPLIB benchmarks, LLM-HCJG attains consistently low optimality gaps, including best or tied-best results on 28 of 29 TSPLIB instances and all 12 CVRPLIB instances. Ablation and structural analyses further indicate that these gains are associated with cross-component compatibility and alignment rather than isolated-component recombination. These results support effective cross-instance transfer within the evaluated routing settings under limited-sample, modest-cost training.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-03 in cs.NE).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE).
Pagina del lavoro: https://arxiv.org/abs/2609.02353 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T05:46:06.397Z dalla pagina https://arxiv.org/catchup/cs.NE/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).