Author or contributor

An improved ant colony optimization algorithm based on context for tourism route planning

Resource type
Authors/contributors
Title
An improved ant colony optimization algorithm based on context for tourism route planning
Abstract
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.
Publication
PLOS ONE
Volume
16
Issue
9
Pages
e0257317
Date
2021-9-16
Journal Abbr
PLoS ONE
Language
en
DOI
10.1371/journal.pone.0257317
ISSN
1932-6203
Accessed
4/28/22, 11:41 AM
Library Catalog
USJ Library
Citation
Liang, S., Jiao, T., Du, W., & Qu, S. (2021). An improved ant colony optimization algorithm based on context for tourism route planning. PLOS ONE, 16(9), e0257317. https://doi.org/10.1371/journal.pone.0257317