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The Scientific World Journal
Volume 2014, Article ID 438260, 12 pages
http://dx.doi.org/10.1155/2014/438260
Research Article

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

1College of Computer Science & Information Engineering, Zhejiang Gongshang University, Zhejiang Province, Hangzhou 310018, China
2Institute of Systems Engineering, Huazhong University of Science and Technology, Hubei Province, Wuhan 430074, China

Received 10 November 2013; Accepted 30 December 2013; Published 18 February 2014

Academic Editors: Z. Cui and X. Yang

Copyright © 2014 Tinggui Chen and Renbin Xiao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.