Determining optimal geo-trail using genetic algorithm (Case study: Damavand Mountain, Iran)

Saeedeh Fakhari; Ali Reza Karbalaee; Junye Wang; Saeedeh Fakhari; Ali Reza Karbalaee; Junye Wang

doi:10.3934/geosci.2026018

AIMS Geosciences

2026, Volume 12, Issue 2: 480-498. doi: 10.3934/geosci.2026018

Previous Article Next Article

Research article

Determining optimal geo-trail using genetic algorithm (Case study: Damavand Mountain, Iran)

1.
Department of Geography & Tourism, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
2.
Department of Climatology, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
3.
Faculty of Science and Technology, Athabasca University, Athabasca, AB, T9S 3A3, Canada

Received: 11 November 2025 Revised: 13 March 2026 Accepted: 01 April 2026 Published: 06 May 2026

Geographic locations and geo-trails are often dispersed, and their accessibility is subject to rapid changes, which can have detrimental effects on the environment, tourism, and economy. Geo-trail planning faces challenges due to geographic dispersion and varying accessibility, impacting sustainable tourism, environmental conservation, and visitor safety. This study aimed to identify the optimal geo-trail route of 12 distinct geo-sites for climbers, eco-tourists, and general tourists in the Mount Damavand region, Iran. A genetic algorithm (GA) was used to solve this multi-objective geo-trail route optimization. The GA model adhered to route connectivity and non-repetition constraints through minimizing total distance, travel time, and cost while maximizing access to services and key attractions from the perspectives of tourists and eco-tourists. The model was implemented in MATLAB (population size: 50; iterations: 100; mutation probability: 0.5) and integrated with ArcGIS for spatial analysis. The GA algorithm converged to a stable solution with an objective function value of 4.718, improved from an initial average of 8.74. The optimal route spanned 105 km and 1033 minutes, connecting key sites including Emamzadeh (S6), Glacier (S10), and Ask (S12). The performance of the GA was benchmarked against three reference approaches: the nearest neighbor (NN) heuristic, a random search baseline, and ant colony optimization (ACO). While both GA and ACO vastly outperformed simple heuristics, the choice between them may depend on specific implementation constraints or desired solution characteristics (e.g., GA's ease of parallelization vs. ACO's faster initial convergence). The GA exhibited greater robustness, with a coefficient variation of 0.9% across runs versus 2.4% for ACO. This demonstrates the effectiveness of GAs in solving complex geotourism routing problems and provides a data-driven framework for sustainable trail planning. The proposed approach enhances visitor experience, supports intelligent tourism management, and minimizes environmental impacts, offering a scalable model for mountainous and ecologically sensitive regions.
- genetic algorithm,
- geotourism,
- geo-trail,
- Damavand Mountain,
- geo-trail optimization
Citation: Saeedeh Fakhari, Ali Reza Karbalaee, Junye Wang. Determining optimal geo-trail using genetic algorithm (Case study: Damavand Mountain, Iran)[J]. AIMS Geosciences, 2026, 12(2): 480-498. doi: 10.3934/geosci.2026018

Related Papers:

Abstract

Geographic locations and geo-trails are often dispersed, and their accessibility is subject to rapid changes, which can have detrimental effects on the environment, tourism, and economy. Geo-trail planning faces challenges due to geographic dispersion and varying accessibility, impacting sustainable tourism, environmental conservation, and visitor safety. This study aimed to identify the optimal geo-trail route of 12 distinct geo-sites for climbers, eco-tourists, and general tourists in the Mount Damavand region, Iran. A genetic algorithm (GA) was used to solve this multi-objective geo-trail route optimization. The GA model adhered to route connectivity and non-repetition constraints through minimizing total distance, travel time, and cost while maximizing access to services and key attractions from the perspectives of tourists and eco-tourists. The model was implemented in MATLAB (population size: 50; iterations: 100; mutation probability: 0.5) and integrated with ArcGIS for spatial analysis. The GA algorithm converged to a stable solution with an objective function value of 4.718, improved from an initial average of 8.74. The optimal route spanned 105 km and 1033 minutes, connecting key sites including Emamzadeh (S6), Glacier (S10), and Ask (S12). The performance of the GA was benchmarked against three reference approaches: the nearest neighbor (NN) heuristic, a random search baseline, and ant colony optimization (ACO). While both GA and ACO vastly outperformed simple heuristics, the choice between them may depend on specific implementation constraints or desired solution characteristics (e.g., GA's ease of parallelization vs. ACO's faster initial convergence). The GA exhibited greater robustness, with a coefficient variation of 0.9% across runs versus 2.4% for ACO. This demonstrates the effectiveness of GAs in solving complex geotourism routing problems and provides a data-driven framework for sustainable trail planning. The proposed approach enhances visitor experience, supports intelligent tourism management, and minimizes environmental impacts, offering a scalable model for mountainous and ecologically sensitive regions.

References

[1]	Hose TA (2020) Historical Viewpoints on the Geotourism Concept in the 21st Century, The Geotourism Industry in the 21st Century, Apple Academic Press, 23–92.
[2]	Pop AC (2012) Ways of Improving Tourism Via Developing Four Cross Competition Tracks. Geografie-UoradeaRo. Analele Univ Oradea Ser Geogr 2: 2–7. Available from: http://geografie-uoradea.ro/Reviste/Anale/Art/2012-2/AUOG_604_Anca.pdf.
[3]	Prieto JLP, de Castro Martínez GF, González EMR (2019) Geotrails in the mixteca alta UNESCO Global Geopark, Oaxaca, Mexico. Cuad Geogr 58: 111–125. https://doi.org/10.30827/cuadgeo.v58i2.7055 doi: 10.30827/cuadgeo.v58i2.7055
[4]	Fennell D (2012) Ecotourism, The Routledge Handbook of Tourism and the Environment, Brock University, St Catharines, Canada: Taylor and Francis Inc., 323–333.
[5]	Eagles PFJ, Haynes CD, McCool SF (2010) Sustainable tourism in protected areas: guidelines for planning and management, Sustain Tour Prot areas Guidel Plan Manag (Russian version).
[6]	Dowling R, Newsome D (2018) Geotourism: Definition, characteristics and international perspectives. Handb Geotourism, 1–22.
[7]	Fakhari S, Saberi M (2021) Geomorphological hazards in geotrails (case study: Damavand region). J Geogr Stud 4: 57–75.
[8]	Timothy DJ, Boyd SW (2015) Tourism and Trails: Cultural, Ecological and Management Issues, Channel View Publications/Multilingual Matters, Bristol, the UK, 231–232.
[9]	Newsome D, Dowling, RK (2010) The future of geotourism where to from here, Geotourism: the tourism of geology and landscape. Wallingford: Goodfellow Publishers Limited. http://dx.doi.org/10.23912/978-1-906884-09-3-1073
[10]	Drápela E (2023) Using a Geotrail for Teaching Geography: An Example of the Virtual Educational Trail "The Story of Liberec Granite". Land 12: 828. https://doi.org/10.3390/land12040828 doi: 10.3390/land12040828
[11]	Williams MA, Rolls S, McHenry MT (2025) Optimising geotrail planning by leveraging least-cost path for sustainable geotourism development: a case study on a Tasmanian west coast post-mining landscape. Inf Technol Tourism 27: 477–512. https://doi.org/10.1007/s40558-024-00308-w doi: 10.1007/s40558-024-00308-w
[12]	Duran S, Doğan M (2026) Assessment of the Accessibility of Potential Geosites and GIS-Based Design of Optimal Georoutes in Ardahan Province (Türkiye). Geoheritage 18: 42. https://doi.org/10.1007/s12371-026-01270-1 doi: 10.1007/s12371-026-01270-1
[13]	Robinson AM (2022) The History of Geopark Development in Australia, A New Way Forward. Geoconserv Res 5: 89–107. https://doi.org/10.30486/gcr.2021.1935126.1096 doi: 10.30486/gcr.2021.1935126.1096
[14]	Reynard E (2004) Géotopes, géo(morpho)sites et paysages gé omorphologiques.
[15]	Fung CK, Jim CY (2015) Segmentation by motivation of Hong Kong Global Geopark visitors in relation to sustainable nature-based tourism. Int J Sustain Dev World Ecol 22: 76–88. https://doi.org/10.1080/13504509.2014.999262 doi: 10.1080/13504509.2014.999262
[16]	Santarém F, Silva R, Santos P (2015) Assessing ecotourism potential of hiking trails: A framework to incorporate ecological and cultural features and seasonality. Tour Manag Perspect 16: 190–206. https://doi.org/10.1016/j.tmp.2015.07.019 doi: 10.1016/j.tmp.2015.07.019
[17]	Stolz J, Megerle HE (2022) Geotrails as a medium for education and geotourism: Recommendations for quality improvement based on the results of a research project in the Swabian Alb UNESCO Global Geopark. Land 11: 1422. https://doi.org/10.3390/land11091422 doi: 10.3390/land11091422
[18]	Chen L, Liang W (2025) Disentangling multi-factor effects via graph contrastive learning for travel recommendation. Appl Soft Comput 186: 114095. https://doi.org/10.1016/j.asoc.2025.114095 doi: 10.1016/j.asoc.2025.114095
[19]	Chen L, Zhu X, Zhu G (2025) Exploiting attributes and keywords for session-based recommendation with multi-view graph neural network. Expert Syst Appl 296: 128990. https://doi.org/10.1016/j.eswa.2025.128990 doi: 10.1016/j.eswa.2025.128990
[20]	Damos MA, Zhu J, Li W, et al. (2024) Enhancing the K-means algorithm through a genetic algorithm based on survey and social media tourism objectives for tourism path recommendations. ISPRS Int J Geo-Inf 13: 40. https://doi.org/10.3390/ijgi13020040 doi: 10.3390/ijgi13020040
[21]	Damos, MA, Xu, W, Zhu, J, et al. (2025) An Efficient Tourism Path Approach Based on Improved Ant Colony Optimization in Hilly Areas. ISPRS Int J Geo-Inf 14: 34. https://doi.org/10.3390/ijgi14010034 doi: 10.3390/ijgi14010034
[22]	Deng Y, Hou M, Zhao B, et al. (2025) New tourism route planning for the Anyue grottoes in Sichuan, China: Fusion of personalization and data analysis. Int J Appl Earth Obs Geoinf 140: 104603. https://doi.org/10.1016/j.jag.2025.104603 doi: 10.1016/j.jag.2025.104603
[23]	Cao S (2022) An Optimal Round‐Trip Route Planning Method for Tourism Based on Improved Genetic Algorithm. Comput Intell Neurosci 2022: 7665874. https://doi.org/10.1155/2022/7665874 doi: 10.1155/2022/7665874
[24]	Ochelska-Mierzejewska J, Poniszewska-Marańda A, Marańda W (2021) Selected genetic algorithms for vehicle routing problem solving. Electronics 10: 3147. https://doi.org/10.3390/electronics10243147 doi: 10.3390/electronics10243147
[25]	Damos MA, Zhu J, Li W, et al. (2021) A novel urban tourism path planning approach based on a multiobjective genetic algorithm. ISPRS Int J Geo-Inf 10: 530. https://doi.org/10.3390/ijgi10080530 doi: 10.3390/ijgi10080530
[26]	Lian B, Yan H, Wang J (2022) Performance analysis of three heuristic algorithms for airfoil design optimization. Int J Green Energy 19: 349–364. https://doi.org/10.1080/15435075.2021.1946813 doi: 10.1080/15435075.2021.1946813
[27]	Haupt RL, Haupt SE (2004) Practical genetic algorithms, John Wiley & Sons.
[28]	Grefenstette JJ (1986) Optimization of Control Parameters for Genetic Algorithms. IEEE Trans Syst Man Cybern 16: 122–128. https://doi.org/10.1109/TSMC.1986.289288 doi: 10.1109/TSMC.1986.289288
[29]	Srinivas M, Patnaik LM (2002) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24: 656–667. https://doi.org/10.1109/21.286385 doi: 10.1109/21.286385
[30]	Mitchell M (1998) An Introduction to Genetic Algorithms, MIT Press, Cambridge.
[31]	Larranaga P, Kuijpers CM, Murga RH, et al. (1999) Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artif Intell Rev 13: 129–170. https://doi.org/10.1023/A:1006529012972 doi: 10.1023/A:1006529012972
[32]	Golberg DE (1989) Genetic algorithms in search, optimization, and machine learning, Addion wesley, 36.
[33]	Potvin JY (1996) Genetic Algorithms for the Traveling Salesman Problem. Ann Oper Res 63: 337–370. https://doi.org/10.1007/BF02125403 doi: 10.1007/BF02125403
[34]	Michalewicz Z (1996) Genetic Algorithms + Data Structures = Evolution Programs, 3^rd edition, Springer-Verlag Berlin Heidelberg GmbH.
[35]	Yaghoobloo Z, Pappalardo G, Mangiameli M (2025) Optimising Cyclist Road-Safety Scenarios Through Angle-of-View Analysis Using Buffer and GIS Mapping Techniques. Infrastructures 10: 184. https://doi.org/10.3390/infrastructures10070184 doi: 10.3390/infrastructures10070184

Reader Comments

Your name:*

Email:*
© 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)