{"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/the-gpu-based-parallel-ant-colony-system","title":"The GPU-based Parallel Ant Colony System","arxiv_id":"1605.02669","date":"2016-05-09","proceeding":null,"authors":["Rafał Skinderowicz"],"abstract":"The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the\nMAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms\ninspired by the behavior of ants. In this article we present three novel\nparallel versions of the ACS for the graphics processing units (GPUs). To the\nbest of our knowledge, this is the first such work on the ACS which shares many\nkey elements of the ACO and the MMAS, but differences in the process of\nbuilding solutions and updating the pheromone trails make obtaining an\nefficient parallel version for the GPUs a difficult task. The proposed parallel\nversions of the ACS differ mainly in their implementations of the pheromone\nmemory. The first two use the standard pheromone matrix, and the third uses a\nnovel selective pheromone memory. Computational experiments conducted on\nseveral Travelling Salesman Problem (TSP) instances of sizes ranging from 198\nto 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536\nCUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS\nrunning on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the\nselective pheromone memory achieved speedups up to 16.85x, but in most cases\nthe obtained solutions were of significantly better quality than for the\nsequential ACS.","url_abs":"http://arxiv.org/abs/1605.02669v2","url_pdf":"http://arxiv.org/pdf/1605.02669v2.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":"the-gpu-based-parallel-ant-colony-system","repo_url":"https://github.com/RSkinderowicz/GPUBasedACS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}