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

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.