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Firefly Algorithm with Proportional Adjustment Strategy

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Data Quality and Trust in Big Data (QUAT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11235))

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Abstract

Firefly algorithm is a new heuristic intelligent optimization algorithm and has excellent performance in many optimization problems. However, in the face of some multimodal and high-dimensional problems, the algorithm is easy to fall into the local optimum. In order to avoid this phenomenon, this paper proposed an improved firefly algorithm with proportional adjustment strategy for alpha and beta. Thirteen well-known benchmark functions are used to verify the performance of our proposed algorithm, the computational results show that our proposed algorithm is more efficient than many other FA algorithms.

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References

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

    Google Scholar 

  2. Jafari, O., Akbari, M.: Optimization and simulation of micrometre-scale ring resonator modulators based on p-i-n diodes using firefly algorithm. Optik 128, 101–112 (2017)

    Article  Google Scholar 

  3. Tuba, E., Mrkela, L., Tuba, M.: Support vector machine parameter tuning using firefly algorithm. In: 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA), pp. 413–418 (2016)

    Google Scholar 

  4. SundarRajan, R., Vasudevan, V., Mithya, S.: Workflow scheduling in cloud computing environment using firefly algorithm. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 955–960 (2016)

    Google Scholar 

  5. Shi, J.Y., et al.: Tracking the global maximum power point of a photovoltaic system under partial shading conditions using a modified firefly algorithm. J. Renew. Sustain. Energy 8, 033501 (2016)

    Article  Google Scholar 

  6. Yang, X.-S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley Publishing, Hoboken (2010)

    Book  Google Scholar 

  7. Yang, X.-S.: Firefly algorithm, Lévy flights and global optimization, 209–218 (2010). https://doi.org/10.1007/978-1-84882-983-1_15

  8. Fister Jr., I., Yang, X.-S., Fister, I., Brest, J.: Memetic firefly algorithm for combinatorial optimization. arXiv preprint arXiv:1204.5165 (2012)

  9. Yu, S., Su, S., Lu, Q., Huang, L.: A novel wise step strategy for firefly algorithm. Int. J. Comput. Math. 91, 2507–2513 (2014)

    Article  MathSciNet  Google Scholar 

  10. Yu, G.: An improved firefly algorithm based on probabilistic attraction. Int. J. Comput. Sci. Math. 7, 530 (2016)

    Article  MathSciNet  Google Scholar 

  11. Wang, H., et al.: Firefly algorithm with adaptive control parameters. Soft. Comput. 21, 5091–5102 (2017)

    Article  Google Scholar 

  12. Liu, C., Zhao, Y., Gao, F., Liu, L.: Three-dimensional path planning method for autonomous underwater vehicle based on modified firefly algorithm. Math. Probl. Eng. 2015, 1–10 (2015)

    Google Scholar 

  13. Goel, S., Panchal, V.K.: Performance evaluation of a new modified firefly algorithm. In: International Conference on Reliability, INFOCOM Technologies and Optimization, pp. 1–6 (2015)

    Google Scholar 

  14. Wang, G., Guo, L., Hong, D., Luo, L., Wang, H.: A modified firefly algorithm for UCAV path planning. Int. J. Hybrid Inf. Technol. 5, 123–144 (2012)

    Google Scholar 

  15. Yu, S., Zhu, S., Ma, Y., Mao, D.: A variable step size firefly algorithm for numerical optimization. Appl. Math. Comput. 263, 214–220 (2015)

    MathSciNet  MATH  Google Scholar 

  16. Selvarasu, R., Kalavathi, M.S., Rajan, C.C.A.: SVC placement for voltage constrained loss minimization using self-adaptive firefly algorithm. Arch. Electr. Eng. 62, 649–661 (2013)

    Article  Google Scholar 

  17. Selvarasu, R., Kalavathi, M.S.: TCSC placement for loss minimization using self adaptive firefly algorithm. J. Eng. Sci. Technol. 10, 291–306 (2015)

    Google Scholar 

  18. Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18, 89–98 (2013)

    Article  MathSciNet  Google Scholar 

  19. Jansi, S., Subashini, P.: A novel fuzzy clustering based modified firefly algorithm with chaotic map for MRI brain tissue segmentation. MAGNT Res. Rep. 3(1), 52–58 (2015)

    Google Scholar 

  20. Al-Wagih, K.: Improved firefly algorithm for unconstrained optimization problems. Int. J. Comput. Appl. Technol. Res. 4, 77–81 (2014)

    Google Scholar 

  21. Yu, S., Yang, S., Su, S.: Self-adaptive step firefly algorithm. J. Appl. Math. 2013, 610–614 (2013)

    MathSciNet  MATH  Google Scholar 

  22. Wang, J.: Firefly algorithm with dynamic attractiveness model and its application on wireless sensor networks. Int. J. Wireless Mobile Comput. 13, 223 (2017)

    Article  Google Scholar 

  23. Lin, Y., Wang, L., Zhong, Y., Zhang, C.: Control scaling factor of cuckoo search algorithm using learning automata. Int. J. Comput. Sci. Math. 7, 476–484 (2016)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No.: 61866014, 61862027, 61762040 and 61762041).

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Authors and Affiliations

  1. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, China

    Jing Wang, Guiyuan Liu & William Wei Song

  2. Dalarna University, Borlänge, Sweden

    William Wei Song

Authors
  1. Jing Wang
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  2. Guiyuan Liu
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  3. William Wei Song
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Corresponding author

Correspondence to Jing Wang .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Macquarie University, Sydney, NSW, Australia

    Quan Z. Sheng

  3. Nara Women’s University, Nara, Japan

    Tetsuya Yoshida

  4. Dalarna University, Borlänge, Sweden

    Azadeh Sarkheyli

  5. Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

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Wang, J., Liu, G., Song, W.W. (2019). Firefly Algorithm with Proportional Adjustment Strategy. In: Hacid, H., Sheng, Q., Yoshida, T., Sarkheyli, A., Zhou, R. (eds) Data Quality and Trust in Big Data. QUAT 2018. Lecture Notes in Computer Science(), vol 11235. Springer, Cham. https://doi.org/10.1007/978-3-030-19143-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-19143-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19142-9

  • Online ISBN: 978-3-030-19143-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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