Using Genetic Algorithms to Optimize ACS-TSP
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
The Nature of Landmarks for Real and Electronic Spaces
COSIT '99 Proceedings of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science
Enriching Wayfinding Instructions with Local Landmarks
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Ant Colony Optimization
Knowledge-Based Systems
On a novel ACO-Estimator and its application to the Target Motion Analysis problem
Knowledge-Based Systems
WSCS '08 Proceedings of the IEEE International Workshop on Semantic Computing and Systems
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
A location and action-based model for route descriptions
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
An analysis of direction and motion concepts in verbal descriptions of route choices
COSIT'09 Proceedings of the 9th international conference on Spatial information theory
Including landmarks in routing instructions
Journal of Location Based Services
IEEE Computational Intelligence Magazine
A rule-based genetic algorithm for mapping route descriptions towards map representations
Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place
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This paper introduces an experimental cooperative and stochastic algorithm for the derivation of spatial routes that fits the semantics of a verbal route description in natural environments. The algorithm mimics the behavior of ants, where positive feedbacks consist of pheromone trails, deposited on attractive paths. The novelty of the approach relies on the integration of the semantics of a verbal route description within the heuristic of the search algorithm. A route is modeled using a graph-based description where landmarks and spatial relationships play a central role. The algorithm is experimented and illustrated by a prototype implementation applied to foot orienteering. Preliminary computational experiments show that the ant colony developed and applied to route finding in natural environments performs relatively well when compared with other meta-heuristics.