Scenic Route Planning of Online Rural Tourism Platform Based on Scientific Computing Visualization Algorithm

Abstract

This study explores the application of scientific computing visualization algorithms in optimizing scenic route generation for online rural tourism platforms. By leveraging advanced computational techniques, such as genetic algorithms (GAs) and simulated annealing (SA), the study aims to enhance the tourist experience and promote sustainable rural development. Through a comprehensive methodology encompassing data collection, algorithm development, and user interface design, the study systematically evaluates the effectiveness of algorithmic optimization in creating visually captivating and accessible scenic routes. Key findings reveal that GAs outperform SA in terms of convergence rate and solution quality, highlighting the suitability of evolutionary approaches for complex optimization problems in rural tourism planning. Quantitative analysis of objective function values demonstrates the successful integration of multiple criteria, including route distance, elevation gain, and scenic beauty, in route optimization. User satisfaction surveys further corroborate the positive impact of algorithmically optimized routes, with participants rating the generated routes highly in terms of scenic beauty, accessibility, and overall enjoyment. Discussion focuses on the implications of the study's findings for rural tourism planning, emphasizing the importance of community engagement, policy formulation, and interdisciplinary collaboration in fostering sustainable tourism development. The study's contributions extend beyond academia to inform practical interventions and decision-making processes aimed at enhancing rural tourism experiences and preserving the natural and cultural heritage of rural landscapes. Overall, the study underscores the transformative potential of scientific computing visualization algorithms in shaping the future of rural tourism in the digital age.