Abstract
Adaptive Fireworks Algorithm (AFWA) is an effective algorithm for solving optimization problems. However, AFWA is easy to fall into local optimal solutions prematurely and it also provides a slow convergence rate. In order to improve these problems, the purpose of this paper is to apply two-master sub-population (TMS) and new selection strategy to AFWA with the goal of further boosting performance and achieving global optimization. Our simulation compares the proposed algorithm (TMSFWA) with the FWA-Based algorithms and other swarm intelligence algorithms. The results show that the proposed algorithm achieves better overall performance on the standard test functions.
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
Similar content being viewed by others
References
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010)
Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 2069–2077 (2013)
Zheng, S., Li, J., Tan, Y.: Adaptive fireworks algorithm. In: Proceedings of 2014 IEEE Congress on Evolutionary Computation, Beijing, China, pp. 3214–3221 (2014)
Zheng, S., Tan, Y.: Dynamic search in fireworks algorithm. In: Proceedings of 2014 IEEE Congress on Evolutionary Computation, Beijing, China, pp. 3222–3229 (2014)
Tan, Y.: Fireworks Algorithm Introduction, 1st edn. Science press, Beijing (2015)
Gao, H.Y., Diao, M.: Cultural firework algorithm and its application for digital filters design. Int. J. Model. Ident. Control 14(4), 324–331 (2011)
Janecek, A., Tan, Y.: Using population based algorithms for initializing nonnegative matrix factorization. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6729, pp. 307–316. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21524-7_37
Wen, R., Mi, G.Y., Tan, Y.: Parameter optimization of local-concentration model for spam detection by using fireworks algorithm. In: Proceedings of 4th International Conference on Swarm Intelligence, Harbin, China, pp. 439–450 (2013)
Chen, T.: On the Computational Complexity of Evolutionary Algorithms. University of Science and Technology of China, Anhui, China (2010, in Chinese)
Liang, J., Qu, B., Suganthan, P., et al.: Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization (2013)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)
M, El-Abd.: Testing a particle swarm optimization and artificial bee colony hybrid algorithm on the CEC13 benchmarks. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 2215–2220 (2013)
Zambrano-Bigiarini, M., Clerc, M., Rojas, R.: Standard particle swarm optimization 2011 at CEC2013: a baseline for future PSO improvements. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 2337–2344 (2013)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Padhye, N., Mittal, P., Deb, K.: Differential evolution: performances and analyses. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, pp. 1960–1967 (2013)
Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings of 1996 IEEE International Conference on Evolutionary Computation, Nagoya, Japan, pp. 312–317 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, X., Han, S., Zhao, L., Gong, C. (2017). Adaptive Fireworks Algorithm Based on Two-Master Sub-population and New Selection Strategy. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)