A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems. (June 2015)

• Record Type: Journal Article
• Authors: Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro
• Is Part Of: Bio systems. Volume 132/133(2015:Jun.)
• Page Start: 43
• Page End: 53
• Publication Date: 2015-06
• Subjects: Artificial bee colony algorithm -- Swarm intelligence -- Continuous optimization problems -- Self adaptive mechanism -- CEC 2014 test suite; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2015.05.002
• Languages: English
• ISSNs: 0303-2647

Abstract

Abstract: The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively.