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Enhanced Fireworks Algorithm with an Improved Gaussian Sparks Operator

  • Jinglei Guo
  • Wei LiuEmail author
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)

Abstract

As a population-based intelligence algorithm, fireworks algorithm simulates the firework’s explosion process to solve optimization problem. A comprehensive study on Gaussian spark operator in enhanced fireworks algorithm (EFWA) reveals that the search trajectory is limited by the difference vector and the diversity of swarm is not effectively increased by new sparks adding. An improved version of EFWA (IEFWA) is proposed to overcome these limitations. In IEFWA, a new Gaussian spark operator utilizes the location information of the best firework and randomly selected firework to calculate the center position and explosion amplitude, which enhance the search for potential region. Experiments on 20 well-known benchmark functions are conducted to illustrate the performance of IEFWA. The results turn out IEFWA outperforms EFWA and dynFWA on most testing functions.

Keywords

Fireworks algorithm Gaussian distribution Explosion 
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Notes

Acknowledgment

This work is supported by the self-determined research funds of CCNU from the colleges basic research and operation of MOE (No. CCNU18QN018).

References

  1. 1.
    Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  2. 2.
    Kennedy, J.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Australia, pp. 1942–1948. IEEE (1995)Google Scholar
  3. 3.
    Yang, X.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, London (2008)Google Scholar
  4. 4.
    Yang, X., Deb, S.: Cuckoo search via levy flights. In: IEEE World Congress on Nature & Biologically Inspired Computing, India, pp. 210–214. IEEE (2009)Google Scholar
  5. 5.
    Liu, C., Yan, X.: The wolf colony algorithm and its application. Chin. J. Electron. 20(2), 212–216 (2011)Google Scholar
  6. 6.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  7. 7.
    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).  https://doi.org/10.1007/978-3-642-13495-1_44CrossRefGoogle Scholar
  8. 8.
    Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: IEEE Congress on Evolutionary Computation, Mexico, pp. 2069–2077. IEEE (2013)Google Scholar
  9. 9.
    Yu, C., Tan, Y.: Fireworks algorithm with covariance mutation. In: IEEE Congress on Evolutionary Computation, Japan, pp. 1250–1256. IEEE (2015)Google Scholar
  10. 10.
    Li, J., Zheng, S., Tan, Y.: Adaptive fireworks algorithm. In: IEEE Congress on Evolutionary Computation, China, pp. 3214–3221. IEEE (2014)Google Scholar
  11. 11.
    Zheng, S., Janecek, A., Li, J., Tan, Y.: Dynamic search in fireworks algorithm. In: IEEE Congress on Evolutionary Computation, China, pp. 3222–3229. IEEE (2014)Google Scholar
  12. 12.
    Liu, L., Zheng, S., Tan, Y.: S-metric based multi-objective fireworks algorithm. In: IEEE Congress on Evolutionary Computation, Japan, pp. 1250–1256. IEEE (2015)Google Scholar
  13. 13.
    Ludwing, S., Dawar, D.: Parallelization of enhanced firework algorithm using MapReduce. Int. J. Swarm Intell. Res. 6(2), 32–51 (2015)CrossRefGoogle Scholar
  14. 14.
    Ding, K., Tan, Y.: Attract-repulse fireworks algorithm and its CUDA implementation using dynamic parallelism. Int. J. Swarm Intell. Res. 6(2), 1–31 (2015)CrossRefGoogle Scholar
  15. 15.
    Yu, C., Kelley, L., Zheng, S., et al.: Fireworks algorithm with differential mutation for solving the CEC 2014 competition problems, China, pp. 3238–3245. IEEE (2014)Google Scholar
  16. 16.
    Zheng, Y., Xu, X., Ling, H., et al.: A hybrid fireworks optimization method with differential evolution operators. Neurocomputing 148, 75–82 (2015)CrossRefGoogle Scholar
  17. 17.
    Gao, H., Diao, M.: Cultural firework algorithm and its application for digital filters design. Int. J. Model. Ident. Control 14(4), 324–331 (2011)CrossRefGoogle Scholar
  18. 18.
    Zheng, S., Tan, Y.: A unified distance measure scheme for orientation coding in identification. In: International Conference on Information Science and Technology, China, pp. 979–985. IEEE (2013)Google Scholar
  19. 19.
    Rajaram, R., Palanisamy, K., Ramasamy, S., et al.: Selective harmonic elimination in PWM inverter using fire fly and fireworks algorithm. Int. J. Innov. Res. Adv. Eng. 1, 55–62 (2014)Google Scholar
  20. 20.
    Liu, Z., Feng, Z., Ke, L.: Fireworks algorithm for the multi-satellite control resource scheduling problem. In: IEEE Congress on Evolutionary Computation, Japan, pp. 1280–1286. IEEE (2015)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer ScienceCentral China Normal UniversityWuhanChina

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