Ant colony algorithms are inspired by the collaborative behavior of ants in real life. The ants wander randomly when looking for food but they are attracted to a substance, called pheromone, left by other ants. Pheromone is generated by the ants on their way back to the colony after reaching food. When several ants use a path to reach food, pheromone levels increase up and a path becomes more and more attractive to other ants. However, pheromone evaporates with time. As it takes a longer period of time to travel along a long path, the intensity of the pheromone is lower than for a shorter path. This mechanism has the double advantage of favoring shorter paths and reducing the attraction to local optima.
The format of vehicle routing problem instances :
Number of customers, best-known solution value
For each customer: Customer number, x, y, demand
Train Bayesian neural network by Ant Colony Optimization (ACO) algorithm
MATLAB Code for Forward Communication Artificial Bee Colony
Dorigo, Marco, et al., eds. Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008, Proceedings. Vol. 5217. Springer, 2008.
Dorigo, Marco, and Luca Maria Gambardella. “Ant colony system: a cooperative learning approach to the traveling salesman problem.” IEEE Transactions on evolutionary computation 1.1 (1997): 53-66.
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