Article Sidebar

Submitted
November 24, 2025
Accepted
March 15, 2026
Published
April 30, 2026
Download
PDF
Statistic
Read Counter : 28 Download : 11

Main Article Content

Abstract

This study optimizes tourist routes across 14 destinations in the city of Medan using the Biogeography-Based Optimization (BBO) algorithm. The problem is formulated as a closed-path Traveling Salesman Problem (TSP) with an extension allowing for flexibility in freely selecting the starting point. The route is determined based on the distance between two locations, where the distance is assumed to be asymmetric to account for real-world urban road conditions such as one-way systems, while ignoring traffic conditions and other costs. Simulation results show that even though the starting point is freely determined, the BBO algorithm is still able to consistently produce routes that are close to optimal with stable convergence. The main contribution of this study is the provision of an adaptive and realistic route planning model to support tourism information systems in urban areas.

Keywords

tourism route optimization biogeography-based optimization TSP metaheuristic algorithm

Article Details

License

Copyright (c) 2026 Fidelis Nofertinus Zai, Donni Andreas Nainggolan, Rian Kurnia, Erwin -

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Zai, F. N., Nainggolan, D. A., Kurnia, R. ., & -, E. (2026). Optimizing Medan Tourist Routes Using BiogeographyBased Optimization. EKSAKTA: Journal of Sciences and Data Analysis, 7(1). https://doi.org/10.20885/EKSAKTA.vol7.iss1.art8
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. X. Wu, H. Guan, Y. Han, J. Ma, A tour route planning model for tourism experience utility maximization, Advances in Mechanical Engineering 9 (2017) 168781401773230. https://doi.org/10.1177/1687814017732309.
  2. Ç. Cergibozan, A. S. Tasan, Tourist Route Planning with a Metaheuristic Approach, in: E. Viles, M. Ormazábal, A. Lleó (Eds.), Closing the Gap Between Practice and Research in Industrial Engineering, Springer, 2018: pp. 193–199.
  3. P. Vansteenwegen, W. Souffriau, G. Vanden Berghe, D. Van Oudheusden, The City Trip Planner: An expert system for tourists, Expert Syst Appl 38 (2011) 6540–6546. https://doi.org/10.1016/j.eswa.2010.11.085.
  4. L. Yan, Improved On-Demand Travel Route Planning Model with Interest Fields, Comput Intell Neurosci 2022 (2022) 1–12. https://doi.org/10.1155/2022/6442441.
  5. D. Simon, Biogeography-Based Optimization, IEEE Transactions on Evolutionary Computation 12 (2008) 702–713. https://doi.org/10.1109/TEVC.2008.919004.
  6. B. Santosa, A.L. Safitri, Biogeography-based Optimization (BBO) Algorithm for Single Machine Total Weighted Tardiness Problem (SMTWTP), Procedia Manuf 4 (2015) 552–557. https://doi.org/10.1016/j.promfg.2015.11.075.
  7. M. Tamjidy, S. Paslar, B.T.H.T. Baharudin, T.S. Hong, M.K.A. Ariffin, Biogeography based optimization (BBO) algorithm to minimise non-productive time during hole-making process, Int J Prod Res 53 (2015) 1880–1894. https://doi.org/10.1080/00207543.2014.965356.
  8. Yujun Zheng, Xueqin Lu, Minxia Zhang, Shengyong Chen, Biogeography-Based Optimization: Algorithms and Applications, Springer Nature Singapore, 2018.
  9. H. Ma, D. Simon, Analysis of migration models of biogeography-based optimization using Markov theory, Eng Appl Artif Intell 24 (2011) 1052–1060. https://doi.org/10.1016/j.engappai.2011.04.012.
  10. A.C.F. Peixoto, C.A.C. António, Memetic-Based Biogeography Optimization Model for the Optimal Design of Mechanical Systems, Mathematics 13 (2025) 492. https://doi.org/10.3390/math13030492.
  11. P.K. Giri, S.S. De, S. Dehuri, Adaptive neighbourhood for locally and globally tuned biogeography based optimization algorithm, Journal of King Saud University - Computer and Information Sciences 33 (2021) 453–467. https://doi.org/10.1016/j.jksuci.2018.03.013.
  12. H. Mühlenbein, D. Schlierkamp-Voosen, Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization, Evol Comput 1 (1993) 25–49. https://doi.org/10.1162/evco.1993.1.1.25.
  13. Thomas Bäck, Evolutionary Algorithms in Theory and Practice, Oxford University Press, 1996.
  14. Robert H. MacArthur, E. O. Wilson, The Theory of Biogeography, Princeton University Press, 1976.
No Related Submission Found