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.

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Keywords

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