Artificial Bee Colony (ABC) is one of the algorithms defined by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. It is as simple as particle swarm (PSO) and differential evolution (DE) optimization algorithms, and uses only common control parameters such as colony size and maximum number of cycles. ABC as an optimization tool provides a population-based search procedure in which individuals called food positions are modified by artificial bees over time and the goal of the bee is to discover places of food sources with quantity Of high nectar and nectar. In the ABC system, artificial bees fly into a multidimensional search space and some (employed and observant bees) choose food sources depending on the experience of themselves and their nestmates, and adjust their positions. Some (scouts) fly and choose random sources without using experience. If the amount of nectar of a new source is greater than the previous one in its memory, they memorize the new position and forget the previous one. Thus, the ABC system combines local search methods, carried out by employed and observing bees, with global search methods, managed by spectators and explorers, trying to balance the exploration and exploitation process.
Artificial Bee Colony (ABC) is a metaheuristic algorithm, inspired by the behavior of honeybee forage, and proposed by Derviş Karaboğa in 2005. It is a simple but powerful algorithm and can be used to solve a great variety of practices and Real World optimization problems. For more information on the algorithm Bee Colony Artificial can consult the related article in Wikipedia. Also, the resources, references, news and software for this algorithm, are available on artificial bee colony official website. Since 2005, some members of the intelligent systems research group, the group leader is D.Karaboga, have studied the ABC algorithm and its applications to real-world problems. Karaboga and Basturk have studied the ABC algorithm version for unrestricted numerical optimization problems and its extended version for constrained optimization problems.