Fireworks Algorithm for Optimization

  • Ying Tan
  • Yuanchun Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)


Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed for global optimization of complex functions. In the proposed FA, two types of explosion (search) processes are employed, and the mechanisms for keeping diversity of sparks are also well designed. In order to demonstrate the validation of the FA, a number of experiments were conducted on nine benchmark test functions to compare the FA with two variants of particle swarm optimization (PSO) algorithms, namely Standard PSO and Clonal PSO. It turns out from the results that the proposed FA clearly outperforms the two variants of the PSOs in both convergence speed and global solution accuracy.


natural computing swarm intelligence fireworks algorithm particle swarm optimization function optimization 


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  1. 1.
    Garnier, S., Gautrais, J., Theraulaz, G.: The biological principles of swarm intelligence. Swarm Intelligence 1(1), 3–31 (2007)CrossRefGoogle Scholar
  2. 2.
    Das, S., Abraham, A., Konar, A.: Swarm intelligence algorithms in bioinformatics. Studies in Computational Intelligence 94, 113–147 (2008)CrossRefGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Proceedings of NATO Advanced Workshop on Robots and Biological Systems (1989)Google Scholar
  7. 7.
    Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 120–127 (2007)Google Scholar
  8. 8.
    Tan, Y., Xiao, Z.M.: Clonal particle swarm optimization and its applications. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 2303–2309 (2007)Google Scholar
  9. 9.
    Perlibakas, V.: Distance measures for PCA-based face recognition. Pattern Recognition Letters 25(6), 711–724 (2004)CrossRefGoogle Scholar
  10. 10.
    Lu, G., Tan, D., Zhao, H.: Improvement on regulating definition of antibody density of immune algorithm. In: Proceedings of the 9th International Conference on Neural Information Processing, vol. 5, pp. 2669–2672 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ying Tan
    • 1
  • Yuanchun Zhu
    • 1
  1. 1.Key Laboratory of Machine Perception (MOE), Peking University Department of Machine Intelligence, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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