Abstract
The swarm intelligent algorithms (SIs) are effective and widely used, while the balance between exploitation and exploration directly affects the accuracy and efficiency of algorithms. To cope with this issue, a backbone whale optimization algorithm based on cross-stage evolution (BWOACS) is proposed. BWOACS is mainly composed of three parts: (1) adopts the density peak clustering (DPC) method to actively divide the population into several sub-populations, generates the backbone representatives (BR) during backbone construction stage; (2) determines the deviation placement (DP) by constructing the co-evolution operators (CE), the search space expansion operators (SE) and the guided transfer operators (GT) during bionic evolution strategy stage; (3) realises the bionic optimisation through DP during backbone representatives guiding co-evolution stage. To verify the accuracy and performance of BWOACS, we compare BWOACS with other variants on 9 IEEE CEC 2017 benchmark problems. Experimental results indicate that BWOACS has better accuracy and convergence speed than other algorithms.
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
References
Gao, W., Sheng, H., Wang, J., Wang, S.: Artificial bee colony algorithm based on novel mechanism for fuzzy portfolio selection. IEEE Trans. Fuzzy Syst. 27(5), 966–978 (2018)
García-Nieto, J., Alba, E., Olivera, A.C.: Swarm intelligence for traffic light scheduling: application to real urban areas. Eng. Appl. Artif. Intell. 25(2), 274–283 (2012)
Jiang, R., Yang, M., Wang, S., Chao, T.: An improved whale optimization algorithm with armed force program and strategic adjustment. Appl. Math. Model. 81, 603–623 (2020)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Lim, H., Hwang, T.: User-centric energy efficiency optimization for miso wireless powered communications. IEEE Trans. Wireless Commun. 18(2), 864–878 (2018)
Liu, W., Wang, Z., Yuan, Y., Zeng, N., Hone, K., Liu, X.: A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Trans. Cybern. 51, 1085–1093 (2019)
Luo, H., Krueger, M., Koenings, T., Ding, S.X., Dominic, S., Yang, X.: Real-time optimization of automatic control systems with application to BLDC motor test rig. IEEE Trans. Industr. Electron. 64(5), 4306–4314 (2016)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Olaru, S., Dumur, D.: Avoiding constraints redundancy in predictive control optimization routines. IEEE Trans. Autom. Control 50(9), 1459–1465 (2005)
Oliva, D., Abd El Aziz, M., Hassanien, A.E.: Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl. Energy 200, 141–154 (2017)
Pan, Q.K.: An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling. Eur. J. Oper. Res. 250(3), 702–714 (2016)
Pham, Q.V., Mirjalili, S., Kumar, N., Alazab, M., Hwang, W.J.: Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Trans. Veh. Technol. 69(4), 4285–4297 (2020)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Tian, Y., Zhang, X., Wang, C., Jin, Y.: An evolutionary algorithm for large-scale sparse multiobjective optimization problems. IEEE Trans. Evol. Comput. 24(2), 380–393 (2019)
Wang, Z.J., et al.: Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling. IEEE Trans. Cybern. 50(6), 2715–2729 (2019)
Yan, Z., Zhang, J., Zeng, J., Tang, J.: Nature-inspired approach: an enhanced whale optimization algorithm for global optimization. Math. Comput. Simul. 185, 17–46 (2021)
Zhang, A., Sun, G., Ren, J., Li, X., Wang, Z., Jia, X.: A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans. Cybern. 48(1), 436–447 (2016)
Zhang, X., Tian, Y., Cheng, R., Jin, Y.: A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 97–112 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, X., Wang, L., Zhang, Z., Han, X., Yue, L. (2022). A Backbone Whale Optimization Algorithm Based on Cross-stage Evolution. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-09677-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09676-1
Online ISBN: 978-3-031-09677-8
eBook Packages: Computer ScienceComputer Science (R0)