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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 195-199 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.112

An Efficient Multi Population Artificial Bee Colony

Vahid Zeighami, Reza Akbari, and Koorush Ziarati
Abstract—Artificial Bee Colony is an optimization algorithm that can be applied on a wide range of engineering problems. In this work, the standard ABC is extended by incorporating cooperative behaviors and an efficient algorithm called multi population ABC (or MPABC) is developed. MPABC aims at improving the performance of the standard ABC algorithm using benefits of cooperation as a social behavior. MPABC works by employing multiple populations that concurrently optimize the solution vector. Cooperation is obtained by sharing information between populations. The proposed algorithm was tested on a set of well known test functions. The results showed that the proposed algorithm is efficient, robust, produce good results, and outperforms other algorithms investigated in this paper.

Index Terms—Artificial bee colony; multi population artificial bee colony; cooperative behaviors.

Vahid Zeighami is with Department of Mathematics, Shiraz University, Shiraz, Iran (e-mail: vahid.zeighami@gmail.com). Reza Akbari is with Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran (e-mail: akbari@sutech.ac.ir). Koorush Ziarati is with Department of Computer Science and Engineering and Information Technology, Shiraz University, Shiraz, Iran (e-mail: ziarati@shirazu.ac.ir).

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Cite:Vahid Zeighami, Reza Akbari, and Koorush Ziarati, "An Efficient Multi Population Artificial Bee Colony," International Journal of Machine Learning and Computing vol.2, no. 3, pp. 195-199, 2012.

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