What do foraging ants and long-distance truck drivers have in common? Both want to deliver their loads as quickly as possible. But while the insects invariably take the shortest path to their destination, their human counterparts often choose a more circuitous route. Thanks to new software that imitates the insects' behavior, truckers can be just as efficient as ants.
The software finds the quickest route among a number of different destinations thanks to so-called "ant colony optimization" (ACO) algorithms, mathematical formulae that mimic the behaviour of ants searching for food. Ants deposit a trail of pheromones chemicals whose smell can inform or influence the behaviour of other ants along the route they travel in search of food. When a food source is found, the ant that discovered it communicates this information to its peers, who then follow that insect's pheromone trail. As more and more ants travel to the food source the pheromone track becomes thicker and thicker, attracting more and more ants, who in turn deposit their own pheromones, and so on. Eventually the ants abandon other avenues of exploration in favor of the one tried and tested route. When confronted with an obstacle on the preferred path, the ants quickly switch to the next most efficient line to the food.
ACO software solves travel problems in much the same way. To find the shortest distances between a large number of destinations, the program repeatedly issues a set of virtual ants, or agents, which randomly visit all the destinations on their electronic road map and lay virtual pheromone trails. When the agents reach a point that is highly useful a traffic-free zone, for instance they are programmed to release more virtual pheromones. Other agents can follow this marked path, and eventually the best rout is mapped out. Tankers from the Swiss fuel provider Pina Petroli are equipped with onboard ACO computer programs provided by the Lugano-based ISDSIA Institute for Artificial Intelligence. Drivers use the software to determine alternative routes around pile-ups or roadworks.
Yet ant-inspired software isn't only useful in deploying a fleet of trucks. "It can provide solutions to any kind of logistical problem," explains Italian electronics engineer Marco Dorigo, 39, head of the IRIDIA Institute for Artificial Intelligence at Brussels' Université Libre. Dorigo first developed ant algorithms in the late 1980s, and he says the applications range from "optimizing duty rosters" to "finding alternative traffic routes" over congested telephone lines. DaimlerChrysler recently tested how ACO software might improve the production processes at its body paint conveyor belts, while at Unilever ant algorithms have already helped identify the most efficient allocation of mixing and storage tanks and packaging machines.
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