Research on Tourist Route based on a Novel Ant Colony Optimization Algorithm

• Published in: 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) pp. 160 - 163
• Main Authors: Yang, Naixin, Shi, Yuliang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: ACO algorithm; Ant colony optimization; Attraction rating; Convergence; Heuristic algorithms; Mathematical model; Multi-objective decision; Optimization; Planning; Search problems; Travel recommendation
• DOI: 10.1109/ICPICS47731.2019.8942567

Ant Colony Optimization (ACO) algorithm is a kind of subject-based intelligent algorithm with positive feedback mechanism and strong ability to find and optimize. The travel recommendation system is recognized as one of the technologies that can effectively solve the problem of information overload. This paper introduces an improved ant colony algorithm based on multi-objective decision-making to recommend the travel route to meet the needs of users. In this algorithm, the ant uses the attraction rating information that meets the user's needs and the actual distance of the attraction to find the shortest path. In order to prove the accuracy of the algorithm recommendation, an experiment is conducted on a network of attractions. The experimental results show that the algorithm can provide high quality solutions and can be effectively applied in the travel recommendation system.