{"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/a-hybrid-genetic-algorithm-for-the-traveling","title":"A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone","arxiv_id":"1812.09351","date":"2018-12-21","proceeding":null,"authors":["Quang Minh Ha","Yves Deville","Quang Dung Pham","Minh Hoàng Hà"],"abstract":"This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in\nwhich a truck and drone are used to deliver parcels to customers. The objective\nof this problem is to either minimize the total operational cost (min-cost\nTSP-D) or minimize the completion time for the truck and drone (min-time\nTSP-D). This problem has gained a lot of attention in the last few years since\nit is matched with the recent trends in a new delivery method among logistics\ncompanies. To solve the TSP-D, we propose a hybrid genetic search with dynamic\npopulation management and adaptive diversity control based on a split\nalgorithm, problem-tailored crossover and local search operators, a new restore\nmethod to advance the convergence and an adaptive penalization mechanism to\ndynamically balance the search between feasible/infeasible solutions. The\ncomputational results show that the proposed algorithm outperforms existing\nmethods in terms of solution quality and improves best known solutions found in\nthe literature. Moreover, various analyses on the impacts of crossover choice\nand heuristic components have been conducted to analysis further their\nsensitivity to the performance of our method.","url_abs":"http://arxiv.org/abs/1812.09351v1","url_pdf":"http://arxiv.org/pdf/1812.09351v1.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":"a-hybrid-genetic-algorithm-for-the-traveling","repo_url":"https://github.com/ovidiuchile/AEA2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"management","task_name":"Management"},{"task_slug":"traveling-salesman-problem","task_name":"Traveling Salesman Problem"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}