Abstract An improved artificial bee colony (ABC) algorithm is proposed to solve numerical function optimization problems. Inspired by the double evolutionary space of cultural algorithm,the proposed algorithm takes advantage of the normative knowledge of reliability space to guide the search region and control the radius of the local search space self-adaptively. Thus,the convergence speed and the exploitation ability are enhanced. In order to maintain diversity,a dispersal strategy is designed to balance global exploration and local exploitation of population capacity.Moreover,different approaches are used to explore new positions in various evolutionary stages. The experimental results demonstrate that the proposed algorithm outperforms existing artificial bee colony algorithms on a number of standard test functions both in convergence speed and solution quality.
[1] Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report,TR06. Kayseri,Turkey: Erciyes University,2005 [2] Seeley T D. The Wisdom of The Hive: The Social Physiology of Honey Bee Colonies. Cambridge,USA: Harvard University Press,1995 [3] Karaboga D,Basturk B. A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony Algorithm. Journal of Global Optimization,2007,39(3): 459-471 [4] Karaboga D,Basturk B. On the Performance of Artificial Bee Colony Algorithm. Applied Soft Computing,2008,8(1): 687-697 [5] Hu Zhonghua,Zhao Min. Simulation on Traveling Salesman Problem (TSP) Based on Artificial Bees Colony Algorithm. Transactions of Beijing Institute of Technology,2009,29(11): 978-982 (in Chinese) (胡中华,赵 敏.基于人工蜂群算法的TSP仿真.北京理工大学学报,2009,29(11): 978-982) [6] Bi Xiaojun,Wang Yanjiao. Artificial Bee Colony with Fast Convergence. Systems Engineering and Electronics,2011,33(12): 2755-2761 (in Chinese) (毕晓君,王艳娇.加速收敛的人工蜂群算法.系统工程与电子技术,2011,33(12): 2755-2761) [7] Karaboga D,Akay B. Artificial Bee Colony Algorithm on Training Artificial Neural Networks // Proc of the 15th IEEE Signal Processing and Communications Applications Conference. London,UK,2007: 1-4 [8] Duan Haibin,Xu Chunfang,Xing Zhihui. A Hybrid Artificial Bee Colony Optimization and Quantum Evolutionary Algorithm for Continuous Optimization Problems. International Journal of Neural Systems,2010,20(1): 39-50 [9] Zhu Guopu,Kwong S. Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization. Applied Mathematics and Computation,2010,217(7): 3166-3173 [10] Banharnsakun A,Achalakul T,Sirinaovakul B. The Best-so-far Selection in Artificial Bee Colony Algorithm. Applied Soft Computing,2010,11(2): 2888-2901 [11] Li Bao,Zeng Jianchao. Comparison and Analysis of the Selection Mechanism in the Artificial Bee Colony Algorithm // Proc of the 9th International Conference on Hybrid Intelligent Systems. Shenyang,China,2009: 411-416 [12] Gao Weifeng,Liu Sanyang. A Modified Artificial Bee Colony Algorithm. Computers Operations Research,2012,39(3): 687-697 [13] Karaboga D,Akay B. A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation,2009,214(1): 108-132 [14] Reynolds R G,Sverdlik W. Problem Solving Using Cultural Algorithms // Proc of the 1st IEEE Conference on Evolutionary Computation. Orlando,USA,1994,II: 645-650 [15] Guo Yinan,Wang Hui. Overview of Cultural Algorithm. Computer Engineering and Applications,2009,45(9): 41-46 (in Chinese) (郭一楠,王 辉.文化算法研究综述.计算机工程与应用,2009,45(9): 41-46) [16] Chung C J. Knowledge-Based Approaches to Self-Adaptation in Cultural Algorithms. Detroit,USA: Wayne State University Press,1997 [17] Reza A,Alireza M,Koorush Z. A Novel Bee Swarm Optimization Algorithm for Numerical Function Optimization. Communications in Nonlinear Science and Numerical Simulation,2010,15(10): 3142-3155 [18] Zhang Jingqiao,Sanderson A. JADE: Adaptive Differential Evolution with Optional External Archive. IEEE Trans on Evolutionary Computation,2009,13(5): 945-958 [19] Liang J J,Suganthan P N,Deb K. Novel Composition Test Functions for Numerical Global Optimization // Proc of the IEEE International Swarm Intelligence Symposium. Messina,Italy,2005: 68-75 [20] Storn R,Price K. Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization,1997,11(4): 341-359 [21] Liang J J,Qin A K,Suganthan P N,et al. Evaluation of Comprehensive Learning Particle Swarm Optimizer // Proc of the 11th International Conference on Neural Information Processing. Calcutta,India,2004: 230-235
2013, Vol. 26 
