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An Overview of the Last Advances and Applications of Artificial Bee Colony Algorithm
Abstract
Swarm Intelligence is defined as collective behavior of decentralized and self-organized systems of a natural or artificial nature. In the last years and today, Swarm Intelligence has proven to be a branch of Artificial Intelligence that is able to solving efficiently complex optimization problems. Some of well-known examples of Swarm Intelligence in natural systems reported in the literature are colony of social insects such as bees and ants, bird flocks, fish schools, etc. In this respect, Artificial Bee Colony Algorithm is a nature inspired metaheuristic, which imitates the honey bee foraging behaviour that produces an intelligent social behaviour. ABC has been used successfully to solve a wide variety of discrete and continuous optimization problems. In order to further enhance the structure of Artificial Bee Colony, there are a variety of works that have modified and hybridized to other techniques the standard version of ABC. This work presents a review paper with a survey of the modifications, variants and applications of the Artificial Bee Colony Algorithm.
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