Gaussian bare-bones artificial bee colony algorithm

As a relatively new global optimization technique, artificial bee colony (ABC) algorithm becomes popular in recent years for its simplicity and effectiveness. However, there is still an inefficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this drawback, a Gaussian barebones ABC is proposed, where a new search equation is designed based on utilizing the global best solution. Furthermore, we employ the generalized opposition-based learning strategy to generate new food sources for scout bees, which is beneficial to discover more useful information for guiding search. A comprehensive set of experiments is conducted on 23 benchmark functions and a real-world optimization problem to verify the effectiveness of the proposed approach. Some well-known ABC variants and state-of-the-art evolutionary algorithms are used for comparison. The experiCommunicated by V. Loia. X. Zhou (B) School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China e-mail: [email protected] Z. Wu State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China e-mail: [email protected] H. Wang School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China e-mail: [email protected] S. Rahnamayan Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology (OUIT), 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada e-mail: [email protected] mental results show that the proposed approach offers higher solution quality and faster convergence speed.