Application of Genetic Algorithm in Optimizing Path Selection in Tourism Route Planning

Path selection in tourism route planning involves optimizing travel routes to maximize tourist satisfaction and minimize travel time, cost, or other constraints. This task can be complex due to factors like visitor preferences, attraction availability, and travel schedules. Tourism planners employ algorithms, such as Genetic Algorithms (GA) or probability-based approaches, to identify efficient routes by analyzing large datasets. These algorithms evaluate potential paths based on criteria like distance, attraction variety, and user satisfaction, often adapting based on real-time data and user feedback to ensure optimal results. Dynamic programming and probabilistic models can further enhance path selection by consideSring changing conditions and transitional probabilities between destinations, providing tourists with tailored, flexible routes that meet their preferences while adhering to practical constraints. This paper investigates the application of Weighted Ranking Ant Colony Optimization (WRACO) in tourism route planning, aiming to enhance travel experiences by efficiently navigating the complexities of tourism landscapes. WRACO integrates a weighted ranking scheme into the Ant Colony Optimization (ACO) framework, biasing ant decision-making towards more attractive paths. Through a comprehensive analysis of simulation results, WRACO demonstrates its efficacy in iteratively refining travel itineraries, minimizing travel distances while ensuring convergence to optimal or near-optimal solutions. Through simulation, WRACO achieves a significant reduction in travel distances, with the best tour length minimized to 200 units over ten iterations. Comparative analysis with other optimization algorithms reveals WRACO's superiority, showcasing a notably low best tour length and high solution quality.