An Improved Ant Colony Optimization to Developing Hilly Areas Tourism Path under Dynamic Objectives

Abstract Tourism path planning is complex in hilly areas due to a variety of challenges including steep hills, narrow roads, and limited accessibility. Furthermore, when planning tourism paths, hilly areas frequently have distinctive natural potential that must be carefully taken into account such as dynamic objectives. Temperature, pressure, and health state are three of the most important dynamic objectives. In this paper, we proposed a dynamic objectives approach to developing tourism path planning in hilly areas. The main contribution of this approach was the improved Ant Colony Optimization algorithm ACO to establish a new multi-dynamic objectives approach and apply this approach in hilly areas. We have improved ACO by comparing construction tour and updating pheromone methods. This approach has been applied to develop tourist routes in Jebel Marra hilly in western Sudan. Our approach was compared with the genetic algorithm GA and the traditional ACO. Results show the advantage of our approach due to the optimizing time being 0.27 seconds, 0.45 seconds for GA, and 0.40 seconds for traditional ACO.