Sutep TongngamPunyapas Chawaratthanarungsri2024-04-232024-04-232020b212170https://repository.nida.ac.th/handle/662723737/6833Thesis (Ph.D (Computer Science and Information Systems))--National Institute of Development Administration, 2020Most industries focus on how to get the benefits from processing and transmitting even in tourism industry. Technology has been used to meet the need of travelers in order to access more information such as flight, route, hotel, transportation and others by themselves. There are some techniques of computer science that solve about travelling problem such as Artificial Intelligence and Animal stimulation is used such as ant behavior etc. Thus, this research proposes how to apply Ant Colony Optimization with travelling problem. As the result, Brute force was taken into consideration to compare the capabilities of The Ant Colony System. The results obtained from Ant Colony System have some routes equal to the shortest distance of Brutes Force, but some Brute Force routes have shortest distances. When looking at the performance of algorithm, the processing time to generate all possible paths of the Brute Force takes more time than Ant Colony System. The efficiency of the Brute Force algorithm is O(N2)(Christian and Thierry) while Any Colony System Only O(m logm)(Walter,). Using Ant Colony System by adding other conditions such as changing vehicles at each tourist attraction to complete the planning. It can be further expanded into a system of advice tourist for tourist recommended plan.232 leavesapplication/pdfengThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Ants -- Behavior -- Mathematical modelsTravel -- Environmental aspectstext::thesis::doctoral thesis10.14457/NIDA.the.2020.162