{"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/an-empirical-analysis-of-approximation","title":"An Empirical Analysis of Approximation Algorithms for the Euclidean Traveling Salesman Problem","arxiv_id":"1705.09058","date":"2017-05-25","proceeding":null,"authors":["Yihui He","Ming Xiang"],"abstract":"With applications to many disciplines, the traveling salesman problem (TSP)\nis a classical computer science optimization problem with applications to\nindustrial engineering, theoretical computer science, bioinformatics, and\nseveral other disciplines. In recent years, there have been a plethora of novel\napproaches for approximate solutions ranging from simplistic greedy to\ncooperative distributed algorithms derived from artificial intelligence. In\nthis paper, we perform an evaluation and analysis of cornerstone algorithms for\nthe Euclidean TSP. We evaluate greedy, 2-opt, and genetic algorithms. We use\nseveral datasets as input for the algorithms including a small dataset, a\nmediumsized dataset representing cities in the United States, and a synthetic\ndataset consisting of 200 cities to test algorithm scalability. We discover\nthat the greedy and 2-opt algorithms efficiently calculate solutions for\nsmaller datasets. Genetic algorithm has the best performance for optimality for\nmedium to large datasets, but generally have longer runtime. Our\nimplementations is public available.","url_abs":"http://arxiv.org/abs/1705.09058v1","url_pdf":"http://arxiv.org/pdf/1705.09058v1.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":"an-empirical-analysis-of-approximation","repo_url":"https://github.com/yihui-he/TSP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"traveling-salesman-problem","task_name":"Traveling Salesman Problem"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}