Abstract
Finding the global optima of a complex real-world problem has become much more challenging task for evolutionary computation and swarm intelligence. Brain storm optimization (BSO) is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming for solving global optimization problems. In this paper, we propose a Random Grouping BSO algorithm termed RGBSO by improving the creating operation of the original BSO. To reduce the load of parameter settings and balance exploration and exploitation at different searching generations, the proposed RGBSO adopts a new dynamic step-size parameter control strategy in the idea generation step. Moreover, to decrease the time complexity of the original BSO algorithm, the improved RGBSO replaces the clustering method with a random grouping strategy. To examine the effectiveness of the proposed algorithm, it is tested on 14 benchmark functions of CEC2005. Experimental results show that RGBSO is an effective method to optimize complex shifted and rotated functions, and performs significantly better than the original BSO algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Suganthan, P.N., Hansen, N.J, Liang, J.J. et al.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Nanyang Technological University, Singapore, Technical Report (2005)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, pp. 1942–1948 (1995)
Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA (1998)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, Cybernetics B 26(2), 29–41 (1996)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)
Yang, X.: Nature-inspired metaheuristic algorithms, Beckington. Luniver Press, UK (2008)
Passino, K.M.: Bacterial foraging optimization. International Journal of Swarm Intelligence Research 1(1), 1–16 (2010)
Jiang, Q.Y., Wang, L., Hei, X.H. et al.: Optimal approximation of stable linear systems with a novel and efficient optimization algorithm. In: IEEE Congress on Evolutionary Computation, pp. 840–844 (2014)
Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011)
Shi, Y.H.: An optimization algorithm based on brainstorming process. International Journal of Swarm Intelligence Research 2(4), 35–62 (2011)
Sun, C.H., Duan, H.B., Shi, Y.H.: Optimal satellite formation reconfiguration based on closed-loop brain storm optimization. IEEE Computational Intelligence Magazine 8(4), 39–51 (2013)
Duan, H.B., Li, S.T., Shi, Y.H.: Predator-prey based brain storm optimization for DC brushless motor. IEEE Transactions on Magnetics 49(10), 5336–5340 (2013)
Jadhav, H.T., Sharma, U., Patel, J., et al.: Brain storm optimization algorithm based economic dispatch considering wind power. In: IEEE International Conference on Power and Energy, pp. 588–593 (2012)
Shi, Y.H.: Multi-objective optimization based on brain storm optimization algorithm. International Journal of Swarm Intelligence Research 4(3), 1–21 (2013)
Xue, J.Q., Wu, Y.L., Shi, Y.H., Cheng, S.: Brain storm optimization algorithm for multi-objective optimization problems. In: The Third International Conference on Swarm Intelligence, pp. 513–519 (2012)
Liang, J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Cao, Z., Shi, Y., Rong, X., Liu, B., Du, Z., Yang, B. (2015). Random Grouping Brain Storm Optimization Algorithm with a New Dynamically Changing Step Size. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-20466-6_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20465-9
Online ISBN: 978-3-319-20466-6
eBook Packages: Computer ScienceComputer Science (R0)