{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sym-nco-leveraging-symmetricity-for-neural","title":"Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization","arxiv_id":"2205.13209","date":"2022-05-26","proceeding":null,"authors":["Minsu Kim","Junyoung Park","Jinkyoo Park"],"abstract":"Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i.e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers less relying on problem-specific expert domain knowledge (heuristic method) and supervised labeled data (supervised learning method). This paper presents a novel training scheme, Sym-NCO, which is a regularizer-based training scheme that leverages universal symmetricities in various CO problems and solutions. Leveraging symmetricities such as rotational and reflectional invariance can greatly improve the generalization capability of DRL-NCO because it allows the learned solver to exploit the commonly shared symmetricities in the same CO problem class. Our experimental results verify that our Sym-NCO greatly improves the performance of DRL-NCO methods in four CO tasks, including the traveling salesman problem (TSP), capacitated vehicle routing problem (CVRP), prize collecting TSP (PCTSP), and orienteering problem (OP), without utilizing problem-specific expert domain knowledge. Remarkably, Sym-NCO outperformed not only the existing DRL-NCO methods but also a competitive conventional solver, the iterative local search (ILS), in PCTSP at 240 faster speed. Our source code is available at https://github.com/alstn12088/Sym-NCO.","url_abs":"https://arxiv.org/abs/2205.13209v2","url_pdf":"https://arxiv.org/pdf/2205.13209v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sym-nco-leveraging-symmetricity-for-neural","repo_url":"https://github.com/alstn12088/sym-nco","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":{"status":"ok"}}],"tasks":[{"task_slug":"combinatorial-optimization","task_name":"Combinatorial Optimization"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"traveling-salesman-problem","task_name":"Traveling Salesman Problem"}],"methods":[{"method_slug":"sym-nco","method_name":"Sym-NCO"}],"datasets_introduced":[],"methods_introduced":[{"slug":"sym-nco","name":"Sym-NCO","full_name":"Sym-NCO"}],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2205.13209","atlas_url":"https://app.syntology.ai/?focus=2205.13209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13209"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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