Abstract
Ant Colony Optimization (ACO) algorithms are based on the imitation of how ants of a colony find the shortest path between the nest and the food. This result is achieved by stigmergetic information, i.e. ants deposit a chemical substance (the pheromone) on the path they follow and their movement is guided by the amount of pheromone.
The imitation of this simple mechanism is the core of any ACO algorithm. In the present contribution we propose a new pheromone updating technique with the aim of speeding up the resulting algorithm for rendering it suited to a real-time implementation.
The ACO algorithms are very dependent on the specific application of interest. In this contribution the Vehicle Routing Problem is considered and the proposed algorithm is compared with 3 classic pheromone updating methods with respect to known benchmarks.
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Loreto, T., Martinelli, G. (2005). Novel Pheromone Updating Strategy for Speeding up ACO Applied to VRP. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_21
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DOI: https://doi.org/10.1007/1-4020-3432-6_21
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
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