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International Conference on Swarm Intelligence

ICSI 2021: Advances in Swarm Intelligence pp 58–71Cite as

Study on the Random Factor of Firefly Algorithm

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

Abstract

The firefly algorithm (FA) is a swarm intelligence algorithm that mimics the swarm behaviour of the firefly in nature. The idea is simple, and FA is easy to realize. To improve its performance, a new method to control the random factor in FA is proposed in this paper, based on the design idea and mathematical model of FA and a simple experiment. Under the new method, the value of the random factor decreases according to a geometric progression sequence. Twenty common ratios of geometric progression sequences are used to optimize nine standard benchmark functions. The experimental results are analysed by the ANOVA and step-up methods. The analysis shows that the performance of FA improves under the new method to control the random factor.

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References

  1. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) Stochastic Algorithms: Foundations and Applications, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14

  2. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)

    Google Scholar 

  3. Yang, X.-S.: Firefly algorithm, lévy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI, pp. 209–218. Springer, Heidelberg (2010). https://doi.org/10.1007/978-1-84882-983-1_15

  4. Farahani, S.M., Abshouri, A.A., Nasiri, B., Meybodi, M.R.: A Gaussian firefly algorithm. Int. J. Mach. Learn. Comput. 1(5), 448–453 (2011)

    Article  Google Scholar 

  5. Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1, 164–171 (2011)

    Article  Google Scholar 

  6. Yang, X.-S., Hosseini, S.S.S., Gandomi, A.H.: Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl. Soft Comput. 12, 1180–1186 (2012)

    Article  Google Scholar 

  7. Gandomi, A.H., Yang, X.-S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2012)

    Article  MathSciNet  Google Scholar 

  8. Gandomi, A.H., Yang, X.-S., Alavi, A.H.: Mixed variable structural optimization using Firefly Algorithm. Comput. Struct. 89, 2325–2336 (2011)

    Article  Google Scholar 

  9. Abshouri, A.A., Meybodi, M.R.: New firefly algorithm based on multi swarm & learning automata in dynamic environments. In: Proceedings of the 5th Indian International Conference on Artificial Intelligence, pp. 1–5. IEEE (2011)

    Google Scholar 

  10. Horng, M.-H.: Vector quantization using the firefly algorithm for image compression. Expert Syst. Appl. 39, 1078–1091 (2012)

    Article  Google Scholar 

  11. Dunnett, C.W., Tamhane, A.C.: A step-up multiple test procedure. J. Am. Stat. Assoc. 87(417), 162–170 (1992)

    Article  MathSciNet  Google Scholar 

  12. Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium, pp. 120–127. IEEE, Honolulu (2007)

    Google Scholar 

  13. Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE, Anchorage (1998)

    Google Scholar 

  14. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)

    Article  Google Scholar 

  15. Kennedy, J.: Probability and dynamics in the particle swarm. In: Proceedings of the 2004 Congress on Evolutionary Computation, pp. 340–347. IEEE, Portland (2004)

    Google Scholar 

  16. Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 2004(8), 204–210 (2004)

    Article  Google Scholar 

  17. Shan, J., Pan, J.S., Chang, C.K., Chu, S.C., Zheng, S.G.: A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine. ISA Trans. (2021, in press)

    Google Scholar 

  18. Peng, H., Zhu, W., Deng, C., Wu, Z.: Enhancing firefly algorithm with courtship learning. Inf. Sci. 2021(543), 18–42 (2021)

    Article  MathSciNet  Google Scholar 

  19. Tian, M., Bo, Y., Chen, Z., Wu, P., Yue, C.: A new improved firefly clustering algorithm for SMC-PHD filter. Appl. Soft Comput. 2019(85), 105840 (2019)

    Google Scholar 

  20. Dhal, K.G., Das, A., Ray, S., Gálvez, J.: Randomly attracted rough firefly algorithm for histogram based fuzzy image clustering. Knowl.-Based Syst. 216 (2021)

    Google Scholar 

  21. Trachanatzi, D., Rigakis, M., Marinaki, M., Marinakis, Y.: A firefly algorithm for the environmental prize-collecting vehicle routing problem. Swarm Evol. Comput. 57 (2020)

    Google Scholar 

  22. Altabeeb, A.M., Mohsen, A.M., Ghallab, A.: An improved hybrid firefly algorithm for capacitated vehicle routing problem. Appl. Soft Comput. 84 (2019)

    Google Scholar 

  23. Ariyaratne, M.K.A., Fernando, T.G.I., Weerakoon, S.: Solving systems of nonlinear equations using a modified firefly algorithm (MODFA). Swarm Evol. Comput. 48, 72–92 (2019)

    Article  Google Scholar 

  24. Yelghi, A., Köse, C.: A modified firefly algorithm for global minimum optimization. Appl. Soft Comput. 62, 29–44 (2018)

    Article  Google Scholar 

  25. Wang, H., et al.: A hybrid multi-objective firefly algorithm for big data optimization. Appl. Soft Comput. 69, 806–815 (2018)

    Article  Google Scholar 

  26. Zhang, Y., Song, X.-F., Gong, D.-W.: A return-cost-based binary firefly algorithm for feature selection. Inf. Sci. 418–419, 561–574 (2017)

    Article  Google Scholar 

  27. He, L., Huang, S.: Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240, 152–174 (2017)

    Article  Google Scholar 

  28. Wang, H., et al.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382–383 (2017)

    Google Scholar 

  29. Kalantzis, G., Shang, C., Lei, Y., Leventouri, T.: Investigations of a GPU-based levy-firefly algorithm for constrained optimization of radiation therapy treatment planning. Swarm Evol. Comput. 26, 191–201 (2016)

    Article  Google Scholar 

  30. Xiao, L., Shao, W., Liang, T., Wang, C.: A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting. Appl. Energy 167, 135–153 (2016)

    Article  Google Scholar 

  31. Lei, X., Wang, F., Wu, F.-X., Zhang, A., Pedrycz, W.: Protein complex identification through Markov clustering with firefly algorithm on dynamic protein-protein interaction networks. Inf. Sci. 329, 303–316 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Power and Energy, Northwestern Polytechnical University, Xi’an, 710072, China

    Yanping Qiao

  2. Science and Technology on Altitude Simulation Laboratory, Mianyang, 621700, China

    Yanping Qiao

  3. China Ship Scientific Research Center, Wuxi, 214082, China

    Feng Li, Xiaofeng Li & Zhigang Zhou

  4. AECC Sichuan Gas Turbine Establishment, Mianyang, 621700, China

    Cong Zhang

Authors
  1. Yanping Qiao
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  2. Feng Li
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  3. Cong Zhang
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  4. Xiaofeng Li
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  5. Zhigang Zhou
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Editor information

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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Qiao, Y., Li, F., Zhang, C., Li, X., Zhou, Z. (2021). Study on the Random Factor of Firefly Algorithm. 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_6

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  • DOI: https://doi.org/10.1007/978-3-030-78743-1_6

  • Published:

  • 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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