Application of an Improved Ant Colony Algorithm in Coastal Tourism Route Optimization

Abstract

Zhang, W., 2019. Application of an improved ant colony algorithm in coastal tourism route optimization. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 84–87. Coconut Creek (Florida), ISSN 0749-0208.The ant colony algorithm has strong robustness, good adaptability, is easy to combine with other algorithms, and has applications in data mining and cluster analysis. Through the study of a basic ant colony algorithm and coastal travel route planning, two improvements to the ant colony algorithm have put forward, which include the adventurous algorithm and provide a new route-seeking method. The main purpose of this research was to make the ant colony algorithm meet the problem of route planning. The experimental results show that the improved ant colony algorithm achieves good results both in optimal path solution and in dynamic coastal travel route planning. The dynamic planning of the route can effectively realize the load balancing of tourist attractions and, at the same time, guide tourists to current small tourist attractions. It reflects the humanization and intelligence of coastal tourism route planning.