A Hybrid Evolutionary Algorithm for Numerical Optimization Problem

Abstract

The hybrid artificial bee colony (ABC) algorithm with differential evolution (DE) techniques (HABCwDE) is proposed for numerical optimization in this paper. The HABCwDE adopts multiple candidate solution generation strategies (CSGSes) from DE techniques to generate new solutions in the framework of the ABC algorithm. In the HABCwDE algorithm, three CSGSes and three groups of parameter settings are employed. The performance of HABCwDE and some other evolutionary algorithms are tested on 26 state-of-the-art benchmark functions. Experimental results demonstrate that HABCwDE is very competitive, and that it is an effective way to improve the performance of ABC algorithm by employing CSGSes from DE techniques.

Keywords::

• Computational intelligence
• Artificial bee colony
• Differential evolution
• Global optimization
• Particle swarm optimization
• Genetic algorithm

Additional information

Notes on contributors

Yu Xue

Yu Xue is a member of IEEE (92058890), ACM (2270255), and CCF (E200029023M). Hereceived Ph. D. degree from College of Computer Science and Technology, Nanjing University of Aeronautics & Astronautics, China, in 2013. He is a lecturer in the School of Computer and Software, Nanjing University of Information Science and Technology. He is also a post-doctor of Nanjing University of Information Science and Technology. He has published nearly twenty journal and conference papers. His research interests include computational intelligence, internet of things and electronic countermeasure.

Suiming Zhong

Shuiming Zhong received Ph. D. degree from the Hohai University, China, in 2011. He is a lecturer in the School of Computer and Software, Nanjing University of Information Science and Technology, China. He is also a post-doctor of Nanjing University of Information Science and Technology. His research interests include artificial neural networks, machine learning.

Tinghuai Ma

Tinghuai Ma is a professor in Computer Sciences at Nanjing University of Information Science & Technology, China. He received his Bachelor (HUST, China, 1997), Master (HUST, China, 2000), PhD (Chinese Academy of Science, 2003) and was Post-doctoral associate (AJOU University, 2004). His research interests are data mining, Cloud Computing, ubiquitous computing, privacy preserving etc.

Jie Cao

Jie Cao received Ph. D. degree in Management Science and Engineering from Southeast University, Nanjing, China in 2005. He is currently a professor with the School of Economics and Management, Nanjing University of Information Science and Technology, China. His research interests include complex system analysis and management decision, emergency management, information management and information system, financial engineering.