ArticlePloS one2021
An improved ant colony optimization algorithm based on context for tourism route planning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A visual perception-guided framework for camera path planning in large-scale digital twin and AI-generated 3D scenes.PloS one · 2026Article
- Article
- Complete-Coverage Path-Planning Algorithm Based on Transition Probability and Learning Perturbation Operator.Sensors (Basel, Switzerland) · 2025Article
- Personalized tourism recommendation model based on temporal multilayer sequential neural network.Scientific reports · 2025Article
- Article
- A multimodal material route planning problem considering key processes at work zones.PloS one · 2024Article
- Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems.PloS one · 2022Article
- Particle Swarm Algorithm and Its Application in Tourism Route Design and Optimization.Computational intelligence and neuroscience · 2022Article
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Authors and funding
4 authors.
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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.
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