Insect studies on emergent intelligence in swarms of unintelligent actors has practical relevance to distributed computing, robotics, and other applications; for example, foraging insects use pheromone trails to select the shortest paths to food, a strategy that has been used to solve the famous "traveling salesman problem" in computer science. Systems with distributed collective intelligence are more robust because they can adapt quickly to a variety of situations.

Foraging ants select the shortest paths to food. They are so efficient that ant models have been used to solve the famous “traveling salesmen problem,” a classic in computer science, which concerns finding the shortest route that will take a salesman through a group of cities. Successive iterations over path networks (paths that have been discovered) results in the shortest routes getting reinforced and the longest ones getting abandoned. The outcome is an optimal path length for ant foraging.

Also, artificial ants provide the best solution to the classic quadratic assignment problem, in which the manufacture of a number of goods must be assigned to different factories so as to minimize the total distance over which the items need to be transported between facilities. There exist many such “optimization problems”, such as telephone routing. Also, individual robots have been programmed to push a box to a destination without communicating.

In another project, a model that was initially introduced to explain how ants cluster their dead and sort their larvae has become the basis of a new approach for analyzing financial data. “The ant-based approach enables the data to be visualized easily, and it boasts one intriguing feature: the number of clusters emerges automatically from the data, whereas conventional methods usually assume a predefined number of groups into which the data are then fit. Thus, antlike sorting has been effective in discovering interesting commonalties that might otherwise have remained hidden.”

Again using a biological system as a model, scientists have devised a technique for scheduling paint booths in a truck factory. The method optimizes variables like paint usage and time spent, as well as implementing load-sharing between paint booths in the case of breakdowns.

“Indeed, the potential of swarm intelligence is enormous. It offers an alternative way of designing systems that have traditionally required centralized control and extensive preprogramming. It instead boasts autonomy and self-sufficiency, relying on direct or indirect interactions among simple individual agents. Such operations could lead to systems that can adapt quickly to rapidly fluctuating conditions.”