@Article{2019.100000100,
AUTHOR = {Peizhong Liu, Xiaofang Liu, Yanming Luo, Yongzhao Du, Yulin Fan, Hsuan‐Ming Feng},
TITLE = {An Enhanced Exploitation Artificial Bee Colony Algorithm in Automatic  Functional Approximations},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {2},
PAGES = {385--394},
URL = {http://www.techscience.com/iasc/v25n2/39666},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000100}
}