Debugging ants: How ants find the shortest route

Collective foraging in ant colonies is as remarkable in that ants are solving a distributed control and optimization task that is still not fully untravelled. Ants deposit pheromone as they travel, and paths with more pheromone are preferred by succeeding ants. Without any direct communication amongst themselves, ants quickly abandon other trails to concentrate on the shortest one. If the food source moves, or a new path is discovered, ants can still overcome initial bias due to pheromone deposited on an earlier path, and switch to the new path. Nevertheless, important questions remain. How much should be advantage offered by a better path discovered later for the ants to switch to it if an initial preference or bias has already built up on an earlier path ? How much bias is too much ? When multiple food sources are present, ants form multiple trails that seem to optimize overall throughput, if that word may be considered apt. These questions have importance in many real life scenarios. How can information be optimally disseminated ? If a competitors´s products or technologies have a large initial bias, then how much gain or advantage should a new product or technology provide in order to cultivate a large following or market share ? What kind of share in the market should you probabilistically expect for the gain you offer ? In the presence of dynamically changing traffic scenarios, how can packets be routed without centralized command and control to maximize network throughput ? This talk summarizes some of our recent work. First, we showed that for traditional ant colony optimization models, beyond a certain threshold, initial bias cannot be overcome. That is, even if a better path is found, ants will not switch from a longer path that has been frequented for a long enough time. Next, motivated by some knowledge of biological ants, we suggest a biologically motivated ant pheromone update model that guarantees convergence to the shortest path regardless of initi- l bias. The algorithm displays this behaviour even when the food source is moved or path lengths change during foraging. Finally, we examine the utility of such an algorithm in the context of routing in communication networks.