Abstract
Artificial bee colony (ABC) is an efficient global optimizer, which has bee successfully used to solve various optimization problems. However, most of these problems are low dimensional. In this paper, we propose a new multi-population ABC (MPABC) algorithm to challenge large-scale global optimization problems. In MPABC, the population is divided into three subpopulations, and each subpopulation uses different search strategies. During the search, all subpopulations exchange there best search experiences to help accelerate the search. Experimental study is conducted on ten global optimization functions with dimensions 50, 100, and 200. Results show that MPABC is better than three other ABC variants on all dimensions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schmitt, L.M.: Theory of genetic algorithms. Theor. Comput. Sci. 259(1–2), 1–61 (2001)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26, 29–41 (1996)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, engineering Faculty, Computer Engineering Department (2005)
Wang, H., et al.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382–383, 374–387 (2017)
Cui, Z., Sun, B., Wang, G., Xue, Y., Chen, J.: A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J. Parallel Distrib. Comput. 103, 42–52 (2017)
Wang, H., Wu, Z., Rahnamayan, S.: Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft Comput. 15(11), 2127–2140 (2011)
Brest, J., Maučec, M.S.: Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Comput. 15(11), 2157–2174 (2011)
Long, W., Jiao, J., Liang, X., Tang, M.: Inspired grey wolf optimizer for solving large-scale function optimization problems. Appl. Math. Model. 60, 112–126 (2018)
LaTorre, A., Muelas, S., Peña, J.M.: A comprehensive comparison of large scale global optimizers. Inf. Sci. 316, 517–549 (2015)
Mahdavi, S., Shiri, M.E., Rahnamayan, S.: Metaheuristics in large-scale global continues optimization: a survey. Inf. Sci. 295, 407–428 (2015)
Mohapatra, P., Das, K.N., Roy, S.: A modified competitive swarm optimizer for large scale optimization problems. Appl. Soft Comput. 59, 340–362 (2017)
Ali, A.F., Tawhid, M.A.: A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems. Ain Shams Eng. J. 8(2), 191–206 (2017)
Hu, X.M., He, F.L., Chen, W.N., Zhang, J.: Cooperation coevolution with fast interdependency identification for large scale optimization. Inf. Sci. 381, 142–160 (2017)
Akay, B., Karaboga, D.: A modified Artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)
Wang, H., Wu, Z.J., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014)
Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217, 3166–3173 (2010)
Gao, W., Liu, S.: A modified artificial bee colony algorithm. Comput. Oper. Res. 39, 687–697 (2012)
Tang, K., et al.: Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Nature Inspired Computation and Applications Laboratory, USTC, China (2007)
Herrera, F., Lozano, M., Molina, D.: Test suite for the special issue of Soft Computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems. Technical report, University of Granada, Spain (2010)
Wang, H., Rahnamayan, S., Sun, H., Omran, M.G.: Gaussian bare-bones differential evolution. IEEE Trans. Cybern. 43(2), 634–647 (2013)
Acknowledgement
This work was supported by the Science and Technology Plan Project of Jiangxi Provincial Education Department (No. GJJ170994), the National Natural Science Foundation of China (No. 61663028), the Distinguished Young Talents Plan of Jiangxi Province (No. 20171BCB23075), the Natural Science Foundation of Jiangxi Province (No. 20171BAB202035), and the Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing (No. 2016WICSIP015).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, H., Wang, W., Cui, Z. (2018). A New Artificial Bee Colony Algorithm for Solving Large-Scale Optimization Problems. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-05054-2_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05053-5
Online ISBN: 978-3-030-05054-2
eBook Packages: Computer ScienceComputer Science (R0)