IMPLEMENTATION AND EVALUATION OF HEURISTIC ALGORITHMS FOR REAL-TIME TOURIST ROUTE PLANNING

Abstract

Real-time tourist route planning is increasingly vital in modern tourism, necessitating algorithms that adapt to dynamic conditions like traffic and user preferences. This study implements and evaluates two heuristic algorithms—genetic algorithms (GA) and simulated annealing (SA)—for real-time route optimization, using Samarkand, Uzbekistan, as a case study, benchmarked against Dijkstra’s algorithm. Experiments across short (5 nodes), medium (7 nodes), and full (10 nodes) tours assessed computation time, fitness score (balancing time, distance, and preference), adaptability, and route quality. Results show Dijkstra excels in speed (0.12-0.25s) and adaptability (93.9-95.3%) but yields lower quality (12.4-23.5). SA offers a balance, with times of 0.87-1.89s, adaptability of 89.2-92.1%, and quality of 14.9-27.8, suitable for mobile applications. GA achieves optimal fitness (42.6-81.5) and quality (15.8-30.2) but lags in speed (2.85-6.74s) and adaptability (82.3-88.7%), favoring pre-planned itineraries. Visualizations, including a Samarkand route map, highlight GA’s preference-rich detours (e.g., Shah-i-Zinda), SA’s balanced paths (e.g., Registan), and Dijkstra’s time focus. The findings suggest SA for real-time use and GA for quality-focused planning, with recommendations for hybrid approaches and real-world validation in heritage tourism contexts.