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Complexity Sciences -
Tools
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click here | | In the real world, ants are able to establish trails to find the shortest path from their nest to a food source. They achieve this not by direct communication but by making small local modifications to their environment – laying down “pheromones”. Eurobios has exploited “ant algorithms” based on this behavior as an optimization technique: they combine local “greedy” behavior, based on an immediate reward, with pheromone-based global reinforcement to lead to excellent solutions to complex problems. In addition, ant algorithms also have the desirable property of being flexible and adaptive with respect to changes in the system. In particular, once learning has occurred on a given problem, ants discover any modifications in the system and find the new optimal solution extremely quickly without needing to start the computations from scratch -- exactly the same way as real ants do when an obstacle is put on their trail.
Eurobios has applied ant algorithms to a variety of optimisation problems: the routing/schedule of airplane flights, supply chain networks, and telecommunication networks.
Case Study: Factory scheduling |
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