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General Information
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 2015 Vol. 5(3): 179-186 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.504

A Hybrid Artificial Bee Colony with Differential Evolution

Chukiat Worasucheep
Abstract—Artificial bee colony (ABC) is a relatively new stochastic algorithm with competitive performance and minimal tuning parameter. This paper proposes a hybrid ABC algorithm with differential evolution (DE), but without additional parameters. DE is a well-known efficient evolutionary algorithm with proven records but its parameter setting is complicated. This proposed hybrid algorithm called ABCDE incorporates the powerful mutation strategies of DE into ABC, in order to increase convergence while diversity is not compromised. The performance of ABCDE is evaluated against both original ABC and opposition-based DE (ODE), a recent DE variant with high performance. The experiment uses twelve widely accepted non-linear benchmark functions with various characteristics, such as difficult landscape, multimodality, shift and rotation, to evaluate the ABCDE’s performance on many complex functions. The experimental results demonstrate a superior performance of ABCDE against original ABC and ODE.

Index Terms—Artificial bee colony, hybridization, differential evolution.

C. Worasucheep is with the King Mongkut University of Technology Thonburi, Bangkok, Thailand (e-mail: chukiat.wor@kmutt.ac.th).


Cite: Chukiat Worasucheep, "A Hybrid Artificial Bee Colony with Differential Evolution," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 179-186, 2015.

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