Firefly Algorithms for Multimodal Optimization

  • Xin-She Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5792)

Abstract

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  2. 2.
    Deb, K.: Optimisation for Engineering Design. Prentice-Hall, New Delhi (1995)Google Scholar
  3. 3.
    Gazi, K., Passino, K.M.: Stability analysis of social foraging swarms. IEEE Trans. Sys. Man. Cyber. Part B - Cybernetics 34, 539–557 (2004)CrossRefGoogle Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimisation and Machine Learning. Addison Wesley, Reading (1989)MATHGoogle Scholar
  5. 5.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  6. 6.
    Kennedy, J., Eberhart, R., Shi, Y.: Swarm intelligence. Academic Press, London (2001)Google Scholar
  7. 7.
    Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization. University Press, Princeton (2001)Google Scholar
  8. 8.
    Shilane, D., Martikainen, J., Dudoit, S., Ovaska, S.J.: A general framework for statistical performance comparison of evolutionary computation algorithms. Information Sciences: An Int. Journal 178, 2870–2879 (2008)CrossRefGoogle Scholar
  9. 9.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)Google Scholar
  10. 10.
    Yang, X.S.: Biology-derived algorithms in engineering optimization. In: Olarius, S., Zomaya, A. (eds.) Handbook of Bioinspired Algorithms and Applications, ch. 32. Chapman & Hall / CRC (2005)Google Scholar
  11. 11.
    Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Chichester (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xin-She Yang
    • 1
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK

Personalised recommendations