An Efficient Tourism Path Approach Based on Improved Ant Colony Optimization in Hilly Areas

The expansion of the tourism industry has led to the development of various methods to find optimal tourism paths. However, planning tourism paths in hilly areas remains complex and has specific challenges. Different algorithms have been used to plan tourism paths in flat and hilly terrains, including the traditional Ant Colony Optimization (ACO). Although widely used, this algorithm faces a number of limitations due to its slow implementation and pheromone update rules. This paper introduces a new approach to overcome these limitations. It presents a method for efficiently optimizing tourism paths in hilly areas based on an improved version of the ACO algorithm. The limitations of the traditional ACO and the Genetic Algorithm (GA) are addressed by improving pheromone updating techniques and implementing new initialization parameters. This approach provides a comprehensive and efficient method for planning hiking trails in hilly regions, considering dynamic tourism objectives such as temperature, atmospheric pressure, and health status. The proposed method is implemented to develop tourist routes in the hilly Jebel Marra region in Western Sudan. A comparison is provided between the effectiveness of this approach and the GA and traditional ACO algorithms. The advantage of the proposed approach is illustrated by results showing an optimization time of 0 points and 27 s compared to 0 points and 45 s and 0 points and 40 s for GA and ACO, respectively.