Open AccessAn improved ant colony optimization algorithm based on context for tourism route planning
Author(s) -
Shengbin Liang,
Tongtong Jiao,
Wencai Du,
Shenming Qu
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0257317
Subject(s) - route planning , tourism , ant colony optimization algorithms , computer science , convergence (economics) , context (archaeology) , shortest path problem , path (computing) , operations research , process (computing) , mathematical optimization , algorithm , geography , mathematics , theoretical computer science , economics , archaeology , economic growth , programming language , operating system , graph
To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.