Authors: Jagdish Chand Bansal; Harish Sharma; Atulya Nagar; K.V. Arya
Addresses: ABV-Indian Institute of Information Technology and Management, Gwalior, Morena Link Road, Gwalior Madhya Pradesh, 474015 India ' ABV-Indian Institute of Information Technology and Management, Gwalior, Morena Link Road, Gwalior Madhya Pradesh, 474015 India ' Centre for Applicable Mathematics and Systems Science (CAMSS), Department of Computer and Mathematical Sciences, Liverpool Hope University, Liverpool L16 9JD, UK ' ABV-Indian Institute of Information Technology and Management, Gwalior, Morena Link Road, Gwalior Madhya Pradesh, 474015 India
Abstract: Artificial bee colony (ABC) optimisation algorithm is relatively a recent and simple population-based probabilistic approach for global optimisation over continuous and discrete spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics when tested over both benchmark and real world problems. ABC, like other probabilistic optimisation algorithms, has inherent drawback of premature convergence or stagnation that leads to the loss of exploration and exploitation capability of ABC. Therefore, in order to find a trade-off between exploration and exploitation capability of ABC algorithm two modifications are proposed in this paper. First, a new control parameter namely, cognitive learning factor (CLF) is introduced in the employed bees phase and onlooker bees phase. Cognitive learning is a powerful mechanism that adjusts the current position of candidate solution by a means of some specified knowledge. Second, the range of ABC control parameter φ is modified. The proposed strategy named as balanced artificial bee colony (BABC) algorithm, balances the exploration and exploitation capability of the ABC. To prove efficiency of the algorithm, it is tested over 24 benchmark problems of different complexities and compared with the basic ABC.
Keywords: numerical optimisation; artificial bee colony optimisation; BCO; cognitive learning factor; CLF; swarm intelligence; exploration; exploitation.
International Journal of Artificial Intelligence and Soft Computing, 2013 Vol.3 No.3, pp.222 - 243
Available online: 18 Apr 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article