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Performance Analysis of the Fireworks Algorithm Versions

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Advances in Swarm Intelligence (ICSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12689))

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Abstract

In the last decades, swarm intelligence algorithms have become a powerful tool for solving hard optimization problems. Nowadays numerous algorithms are proved to be good for different problems. With the overwhelming number of algorithms, it became hard for a common user to choose an appropriate method for solving a certain problem. To provide guidelines, it is necessary to classify optimization metaheuristics according to their capabilities. Deep statistical comparison represents a novel method for comparing and analyzing optimization algorithms. In this paper, the deep statistical comparison method was used for comparing different versions of the widely used fireworks algorithm. The fireworks algorithm was developed and improved in the last ten year, and this paper provides a theoretical analysis of five different versions, a cooperative framework for FWA, bare bones FWA, guided FWA, loser-out tournament based FWA, and dynamic search FWA. Based on the obtained results, the loser-out tournament based FWA has the best performance in the term of the solution quality, while the dynamic search FWA is the best in term of the solutions distribution in the search space.

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References

  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)

    Article  Google Scholar 

  2. Eftimov, T., Korošec, P.: A novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of solutions in the search space. Inf. Sci. 489, 255–273 (2019)

    Article  MathSciNet  Google Scholar 

  3. Eftimov, T., Korošec, P., Seljak, B.K.: A novel approach to statistical comparison of meta-heuristic stochastic optimization algorithms using deep statistics. Inf. Sci. 417, 186–215 (2017)

    Article  Google Scholar 

  4. Eftimov, T., Petelin, G., Korošec, P.: DSCTool: a web-service-based framework for statistical comparison of stochastic optimization algorithms. Appl. Soft Comput. 87 (2020)

    Google Scholar 

  5. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  6. Korošec, P., Eftimov, T.: Insights into exploration and exploitation power of optimization algorithm using DSCTool. Mathematics 8(9), 1–11 (2020)

    Article  Google Scholar 

  7. Li, J., Tan, Y.: Loser-out tournament-based fireworks algorithm for multimodal function optimization. IEEE Trans. Evol. Comput. 22(5), 679–691 (2018). https://doi.org/10.1109/TEVC.2017.2787042

    Article  Google Scholar 

  8. Li, J., Zheng, S., Tan, Y.: The effect of information utilization: introducing a novel guiding spark in the fireworks algorithm. IEEE Trans. Evol. Comput. 21(1), 153–166 (2017). https://doi.org/10.1109/TEVC.2016.2589821

    Article  Google Scholar 

  9. Li, J., Tan, Y.: The bare bones fireworks algorithm: a minimalist global optimizer. Appl. Soft Comput. 62, 454–462 (2018). https://doi.org/10.1016/j.asoc.2017.10.046

    Article  Google Scholar 

  10. Liang, J., Qu, B., Suganthan, P., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, and Nanyang Technological University, Singapore, Technical report 201212 (2013)

    Google Scholar 

  11. 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_44

    Chapter  Google Scholar 

  12. Tuba, E., Jovanovic, R., Beko, M., Tallón-Ballesteros, A.J., Tuba, M.: Bare bones fireworks algorithm for medical image compression. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A.J. (eds.) IDEAL 2018. LNCS, vol. 11315, pp. 262–270. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03496-2_29

    Chapter  Google Scholar 

  13. Tuba, E., Jovanovic, R., Hrosik, R.C., Alihodzic, A., Tuba, M.: Web intelligence data clustering by bare bone fireworks algorithm combined with k-means. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, p. 7. ACM (2018)

    Google Scholar 

  14. Tuba, E., Strumberger, I., Bacanin, N., Jovanovic, R., Tuba, M.: Bare bones fireworks algorithm for feature selection and SVM optimization. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 2207–2214. IEEE (2019)

    Google Scholar 

  15. Tuba, E., Tuba, M., Beko, M.: Support vector machine parameters optimization by enhanced fireworks algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2016. LNCS, vol. 9712, pp. 526–534. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41000-5_52

    Chapter  Google Scholar 

  16. Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inform. Control 26(1), 33–42 (2017)

    Article  Google Scholar 

  17. Tuba, E., Tuba, M., Simian, D.: Wireless sensor network coverage problem using modified fireworks algorithm. In: 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 696–701. IEEE (2016)

    Google Scholar 

  18. Tuba, E., Tuba, M., Simian, D., Jovanovic, R.: JPEG quantization table optimization by guided fireworks algorithm. In: Brimkov, V.E., Barneva, R.P. (eds.) IWCIA 2017. LNCS, vol. 10256, pp. 294–307. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59108-7_23

    Chapter  Google Scholar 

  19. Zheng, S., Janecek, A., Li, J., Tan, Y.: Dynamic search in fireworks algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 3222–3229. IEEE (2014)

    Google Scholar 

  20. Zheng, S., Li, J., Janecek, A., Tan, Y.: A cooperative framework for fireworks algorithm. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 27–41 (2015). https://doi.org/10.1109/TCBB.2015.2497227

    Article  Google Scholar 

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Acknowledgement

The authors thank Tome Eftimov and Peter Korošec for sharing the DSC tool and providing the statistical results.

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Ira Tuba, Ivana Strumberger, Eva Tuba, Nebojsa Bacanin & Milan Tuba

Authors
  1. Ira Tuba
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  2. Ivana Strumberger
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  3. Eva Tuba
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  4. Nebojsa Bacanin
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  5. Milan Tuba
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Corresponding author

Correspondence to Milan Tuba .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Tuba, I., Strumberger, I., Tuba, E., Bacanin, N., Tuba, M. (2021). Performance Analysis of the Fireworks Algorithm Versions. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_37

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  • DOI: https://doi.org/10.1007/978-3-030-78743-1_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78742-4

  • Online ISBN: 978-3-030-78743-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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