The bees algorithm mimics the behavior of honeybees to accomplish searches, prioritization, and other tasks. It was developed in 2005, and has been applied to a range of optimization problems. The goal is to determine the best solution to a problem, whether it’s a search query or the allocation of resources. Decision making processes used by bees in nature to solve problems pertaining to hive management can be equally effective in other environments.
An individual beehive uses a combination of two search methods to return data; in this case, information about food sources. The first is the use of scouts, who scan a region randomly to locate specific areas, or neighborhoods, that are likely to yield good results. Scouts report back to the hive, and the other bees decide which neighborhoods to search more intensively to locate useful resources. This combination of random and local search patterns can be optimal for some search environments.
In the bees algorithm, the programmer can decide how many scouts to send out, casting them out to make random searches in all directions. They locate the most likely sources of useful data, or the most optimal solutions in an array of choices, and report back with this data. More intensive localized searches in these regions can return the best results, ranked in terms of relevance, effectiveness, and other characteristics the programmer may set.
This is an example of swarm intelligence, where an algorithm involves the creation of a group of entities that work together to solve a problem. This can differ from more linear algorithms, which move through a series of steps to find the best results. Using the bees algorithm can allow researchers, managers, and other people with questions they need answered to quickly sift through a large library of possible results to return the best, and rank these by preference to determine which to pursue.
Human operators aren’t the only ones who can use the bees algorithm. Automated systems can also use it in their decision making processes. This flexible algorithm can provide a range of options, allowing the system to select the best one to resolve a given challenge. For advanced robotics, the creation of neural nets, and similar topics, the bees algorithm offers a number of complex and functional applications. Researchers can also evaluate the success of various outcomes to teach the algorithm how to behave in the future.
