Travel Route Planning Based on Intelligent Optimization Algorithms

Travel serves as an essential means for individuals to relax and unwind from work-related stress. The selection of appropriate destinations and the planning of efficient itineraries not only enhance personal experience but also improve the operational efficiency of travel agencies and other commercial entities. To address these concerns, this paper introduces a comprehensive multi-criteria evaluation method that integrates the Entropy Weight Method (EWM) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to optimize the selection of travel destinations. Subsequently, a hybrid approach combining the Ant Colony Algorithm (ACA) and Simulated Annealing Algorithm (SAA) is employed to plan optimal travel routes. Utilizing publicly available data from 352 tourist cities, multiple simulations of the proposed methodology have been executed. The numerical results indicate that this approach effectively aids individuals in selecting suitable travel destinations and planning efficient travel routes.