All living organisms must solve ecological problems to adapt to their environment. The best solutions are selected in the course of evolution. Many of these solutions are robust and worthy of study. For example, honey bees have a complex social organization that helps them solve the problem of foraging for food. The components of the foraging system are the initial foragers, who leave the hive and investigate the environment to locate food sources. Another group of foragers await the return of the initial foragers in the “dance area” of the hive. Returning foragers communicate quality and location of food sources by means of the waggle dance. Additional foragers are recruited to collect food from the source.Karaboga* developed the Aritficial Bee Colony (ABC) algorithm based on honey bee foragers. The model consists of Scout bees, Onlooker Bees and Employed Bees. The “Employed Bees” forage for solutions to the problem, then report to the Onlooker Bees. The Onlooker Bees further investigate the neighborhood for hidden solutions in an iterative fashion. A solution is replaced if a better solution is found and a new iteration made. If after several iterations, a better solution is not found, the location is abandoned, the employed bee becomes a “scout bee” and searches for solutions in other neighborhoods.
The ABC algorithm works surprisingly well for a number of optimizing problems. Optimization algorithms run silently in many of the computer functions we routinely use. Insects have evolved some elegant solutions to optimization problems that are worthy of emulating.
*B. Basturk, D. Karaboga, An artificial bee colony (ABC) algorithm for numeric function optimization, in: Proceedings of the IEEE swarm intelligence, symposium, 2006, pp. 12–14.