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Introduction to Fireworks Algorithm

Author

Listed:
  • Ying Tan

    (School of Electronics Engineering and Computer Science, Peking University, Beijing, China)

  • Chao Yu

    (School of Electronics Engineering and Computer Science, Peking University, Beijing, China)

  • Shaoqiu Zheng

    (School of Electronics Engineering and Computer Science, Peking University, Beijing, China)

  • Ke Ding

    (School of Electronics Engineering and Computer Science, Peking University, Beijing, China)

Abstract

Inspired by fireworks explosion at night, conventional fireworks algorithm (FWA) was developed in 2010. Since then, several improvements and applications were proposed to improve the efficiency of FWA. In this paper, the conventional fireworks algorithm is first summarized and three improved fireworks algorithms are provided. By changing the ways of calculating the numbers and amplitudes of sparks in fireworks' explosion, the improved FWA algorithms become more reasonable and explainable. In addition, the multi-objective fireworks algorithm and the graphic processing unit (GPU) based fireworks algorithm are also presented, particularly the GPU based fireworks algorithm is able to speed up the optimization process considerably. Extensive experiments on 13 benchmark functions demonstrate that the three improved fireworks algorithms significantly increase the accuracy of found solutions, yet decrease the running time dramatically. At last, some applications of fireworks algorithm are briefly described, while its shortcomings and future research directions are identified.

Suggested Citation

  • Ying Tan & Chao Yu & Shaoqiu Zheng & Ke Ding, 2013. "Introduction to Fireworks Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 4(4), pages 39-70, October.
    • Handle: RePEc:igg:jsir00:v:4:y:2013:i:4:p:39-70
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    Cited by:

    1. Yimeng Shi & Hongyuan Zhang & Zheng Chen & Yueyue Sun & Xuecheng Liu & Jin Gu, 2023. "A Study on the Deployment of Mesoscale Chemical Hazard Area Monitoring Points by Combining Weighting and Fireworks Algorithms," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    2. Asaju La’aro Bolaji & Aminu Ali Ahmad & Peter Bamidele Shola, 2018. "Training of neural network for pattern classification using fireworks algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 208-215, February.

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