Open Access
ARTICLE
An Enhanced Exploitation Artificial Bee Colony Algorithm in Automatic Functional Approximations
Peizhong Liu1, Xiaofang Liu1, Yanming Luo2, Yongzhao Du1, Yulin Fan1, Hsuan‐Ming Feng3
1 College of Engineering, Huaqiao University, Quanzhou 362021-China
2 College of Computer Science and Technology, Huaqiao University, Xiamen 361021–China
3 Department of Computer Science and Information Engineering, National Quemoy University, No.1 University Rd, Kin-Ning Vallage Kinmen, 892 Taiwan, R.O.C.
* Corresponding Authors: Hsuan‐Ming Feng, ,
Intelligent Automation & Soft Computing 2019, 25(2), 385-394. https://doi.org/10.31209/2019.100000100
Abstract
Aiming at the drawback of artificial bee colony algorithm (ABC) with slow
convergence speed and weak exploitation capacity, an enhanced exploitation
artificial bee colony algorithm is proposed, EeABC for short. Firstly, a
generalized opposition-based learning strategy (GOBL) is employed when initial
population is produced for obtaining an evenly distributed population.
Subsequently, inspired by the differential evolution (DE), two new search
equations are proposed, where the one is guided by the best individuals in the
next generation to strengthen exploitation and the other is to avoid premature
convergence. Meanwhile, the distinction between the employed bee and the
onlooker bee is not made, unified as a bee and controlled by the probability P.
The performance of proposed approach was examined on 14 benchmark
functions, and results are compared with basic ABC and other ABC variants. As
documented in the experimental results, the proposed algorithm has good
optimization performance and can improve both the accuracy and the
convergence speed.
Keywords
Cite This Article
P. Liu, X. Liu, Y. Luo, Y. Du, Y. Fan
et al., "An enhanced exploitation artificial bee colony algorithm in automatic functional approximations,"
Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 385–394, 2019. https://doi.org/10.31209/2019.100000100