Handling Optimization Problem, and the Scope of Varied Artificial Bee Colony (ABC) Algorithms: A Contemporary Research
M Rao Batchanaboyina1, Naga Raju Devarakonda2

1M Rao Batchanaboyina, Research Scholar, Department of Computer Science Engineering, ANU College of Engineering & Technology, Guntur, Andhra Pradesh, India.

2Naga Raju Devarakonda, Department of Information Technlogy, Lakireddi Bali Reddy College of Engineering Mylavaram, Andhra Pradesh, India.

Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 July 2019 | PP: 607-611 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F11250486S419/19©BEIESP | DOI: 10.35940/ijitee.F1125.0486S419

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: One of the most successful search algorithms of the last decade is Artificial Bee Colony (ABC) algorithm. It was first coined by Dervis Karaboga, 2005. Since then a group of variants of the algorithm have been anticipated to find solutions for the problems of optimization. The motivation for the algorithm is the search process of honey bees for food sources. The present paper aimed to bring out the evolutionary developments of the algorithm that cover numerous versions of the algorithm with the strategic changes to meet the optimization needs of the adopted problem contexts. This survey clearly reviewed the basic types, advancements, application areas, and the relevance of the ABC algorithm addressing various problem contexts. The efforts made by the research community since the last two decades along with the success stories are discussed in detail. The attachment of the optimization process of ABC with data mining is dealt in particular. Finally the opportunities and the scope of the application of the algorithm in large areas of problem domains are highlighted.

Keywords: Swarm Intelligence, ABC Algorithm, Clustering, optimization and Outlier Analysis, particle swarm optimization (PSO).
Scope of the Article: Computer Science and Its Applications