Advertisement

The Improvement on Controlling Exploration and Exploitation of Firework Algorithm

  • Jianhua Liu
  • Shaoqiu Zheng
  • Ying Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

Firework algorithm (FWA) is a new Swarm Intelligence (SI) based optimization technique, which presents a different search manner and simulates the explosion of fireworks to search the optimal solution of problem. Since it was proposed, fireworks algorithm has shown its significance and superiority in dealing with the optimization problems. However, the calculation of number of explosion spark and amplitude of firework explosion of FWA should dynamically control the exploration and exploitation of searching space with iteration. The mutation operator of FWA needs to generate the search diversity. This paper provides a kind of new method to calculate the number of explosion spark and amplitude of firework explosion. By designing a transfer function, the rank number of firework is mapped to scale of the calculation of scope and spark number of firework explosion. A parameter is used to dynamically control the exploration and exploitation of FWA with iteration going on. In addition, this paper uses a new random mutation operator to control the diversity of FWA search. The modified FWA have improved the performance of original FWA. By experiment conducted by the standard benchmark functions, the performance of improved FWA can match with that of particle swarm optimization (PSO).

Keywords

Firework Algorithm Swarm Intelligence Algorithm Exploration and Exploitation PSO 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. Robots and Biological Systems: Towards a New Bionics? 703–712 (1993)Google Scholar
  2. 2.
    De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)CrossRefGoogle Scholar
  3. 3.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(1), 29–41 (1996)CrossRefGoogle Scholar
  4. 4.
    Gao, H., Diao, M.: Cultural firework algorithm and its application for digital filters design. International Journal of Modelling, Identification and Control 14(4), 324–331 (2011)CrossRefGoogle Scholar
  5. 5.
    Janecek, A., Tan, Y.: Iterative improvement of the multiplicative update nmf algorithm using nature-inspired optimization. In: 2011 Seventh International Conference on Natural Computation (ICNC), vol. 3, pp. 1668–1672. IEEE (2011)Google Scholar
  6. 6.
    Janecek, A., Tan, Y.: Swarm intelligence for non-negative matrix factorization. International Journal of Swarm Intelligence Research (IJSIR) 2(4), 12–34 (2011)CrossRefGoogle Scholar
  7. 7.
    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)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)Google Scholar
  9. 9.
    Pei, Y., Zheng, S., Tan, Y., Takagi, H.: An empirical study on influence of approximation approaches on enhancing fireworks algorithm. In: IEEE International Conference on System, Man and Cybernetics (SMC 2012), pp. 14–17. IEEE, Seoul (2012)Google Scholar
  10. 10.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings: IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)Google Scholar
  11. 11.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., et al.: Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. In: 2005 IEEE Congress on Evolution Computation (CEC), pp. 1–15. IEEE (2005)Google Scholar
  13. 13.
    Tan, Y., Xiao, Z.: Clonal particle swarm optimization and its applications. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 2303–2309. IEEE (2007)Google Scholar
  14. 14.
    Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. Advances in Swarm Intelligence pp. 355–364 (2010)Google Scholar
  15. 15.
    Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algirithm. In: IEEE International Conference on Evolutionary Computation. IEEE (submitted, 2013)Google Scholar
  16. 16.
    Zheng, X.X., Y.J., H.F., L.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Accepted by Neurocomputing (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jianhua Liu
    • 1
    • 2
    • 3
  • Shaoqiu Zheng
    • 2
    • 3
  • Ying Tan
    • 2
    • 3
  1. 1.School of Information Science and EngineeringFujian University of TechnologyFuzhouP.R. China
  2. 2.Department of Machine Intelligence, School of EECSPeking UniversityChina
  3. 3.Key Laboratory of Machine Perception (MOE)Peking UniversityChina

Personalised recommendations