Artificial bee colony (ABC) algorithm is one of the most effective and efficient swarm intelligence algorithms for global numerical optimization, which is inspired by the intelligent foraging behavior of honey bees and has shown good performance in most case. However, due to its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. In order to solve this concerning issue, in this paper, we propose a novel artificial bee colony algorithm based on neighboring information learning (called NILABC), in which the employed bees and onlooker bees search candidate food source by learning the valuable information from the best food source among their neighbors. Furthermore, the size of the neighbors is linearly increased with the evolutionary process, which is used to ensure the employed bees and onlooker bees obtain the guidance from the best solution in local area at the early stage and the best solution in the global area at the late stage. Through the comparison of NILABC with the basic ABC and some other variants of ABC on 22 benchmark functions, the experimental results demonstrate that NILABC is better than the compared algorithms on most cases in terms of solution quality, robustness and convergence speed.
Evolutionary algorithm Artificial bee colony algorithm Neighboring information learning Ranking-based probability selection Global numerical optimization
This is a preview of subscription content, log in to check access.
This work is supported by the National Natural Science Foundation of China under Grants 61402291, 61402294, and 61170283, National High-Technology Research and Development Program (863 Program) of China under Grant 2013AA01A212, Ministry of Education in the New Century Excellent Talents Support Program under Grant NCET-12-0649, Foundation for Distinguished Young Talents in Higher Education of Guangdong, China under Grant 2013LYM_0076 and 2014KQNCX129, Major Fundamental Research Project in the Science and Technology Plan of Shenzhen under Grants JCYJ20140828163633977 and JCYJ20140418181958501.
Chuang, Y.C., Chen, C.T., Hwang, C.: A real-code genetic algorithm with a direction-based crossover operator. Inform. Sci. 305, 320–348 (2015)CrossRefGoogle Scholar
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Opt. 39, 459–471 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)CrossRefGoogle Scholar
Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217, 3166–3173 (2010)MathSciNetzbMATHGoogle Scholar