Determining N-Days Tourist Route Using Swap Operator Based Artificial Bee Colony Algorithm

Ayunda Farah Istiqamah, Z K Abdurahman Baizal, Yusza Reditya Murti

Abstract


Traveling is one of the activities chosen by many people to spend holidays. Some tourists want to go on vacation in a place they have never visited before, so they need a tool to plan a tour. Planning this tour includes determining tourist route. We analogize the determination of tourist routes using Traveling Salesman Problem (TSP). The main objective of this study was to find the optimal tourist route using Swap Operator Based Artificial Bee Colony Algorithm. We use Multi-Attribute Utility Theory (MAUT) to accommodate user needs for the route that recommended by the system. The criteria for user preferences used in this study are: 1) routes with as many tourist attractions as possible, 2) routes that pass popular destinations, and 3) routes with minimal costs. Based on the experiment results, Swap Operator Based Artificial Bee Colony gives more optimal results than the Simulated Annealing, especially in terms of the number of tourist attractions (nodes) that can be visited in one trip.

Keywords: Multi-Attribute Utility Theory, Swap Operator Based Artificial Bee Colony Algorithm, Traveling Salesman Problem


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References


M. A. P. Muniandy, L. K. Mee, and L. K. Ooi, “Efficient route planning for travelling salesman problem,” ICOS 2014 - 2014 IEEE Conf. Open Syst., pp. 24–29, 2014.

Z. K. A. Baizal, K. M. Lhaksmana, A. A. Rahmawati, M. Kirom, and Z. Mubarok, “Travel route scheduling based on user ’ s preferences using simulated annealing,” 2018.

S. S. N. Kumbharana, G. Pandey, S. N, and P. G. M. Pandey, “Solving Travelling Salesman Problem using Firefly Algorithm,” Int. J. Res. Sci. Adv. Technol., vol. 2, no. 2, pp. 053–057, 2013.

Y. Shigehiro, T. Katsura, and T. Masuda, “An Application of Particle Swarm Optimization to Traveling Salesman Problem,” SICE Annu. Conf. 2010, pp. 1629–1632, 2010.

F. H. Prabowo, “A Multi-Level Genetic Algorithm Approach for Generating Efficient Travel Plans,” 2018 6th Int. Conf. Inf. Commun. Technol., vol. 0, no. c, pp. 86–91, 2018.

L. Li, Y. Cheng, L. Tan, and B. Niu, “A Discrete Artificial Bee Colony Algorithm for TSP Problem,” pp. 566–573, 2012.

D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, 2009.

D. Karaboga, “A Combinatorial Artificial Bee Colony Algorithm for Traveling Salesman Problem,” pp. 50–53, 2011.

K. Wang, L. A. N. Huang, C. Zhou, and P. G. We, “PARTICLE SWARM OPTIMIZATION FOR TRAVELING SALESMAN PROBLEM,” no. November, pp. 1583–1585, 2003.

D. Karaboga, “AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION,” J. Biol. Chem., vol. 280, no. 40, pp. 33960–33967, 2005.

D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” J. Glob. Optim., vol. 39, no. 3, pp. 459–471, 2007.

L. Chen and P. Pu, “Critiquing-based recommenders: Survey and emerging trends,” User Model. User-adapt. Interact., vol. 22, no. 1–2, pp. 125–150, 2012.




DOI: http://dx.doi.org/10.21108/INDOJC.2020.5.1.382

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