Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.


Particle Swarm Optimization Harmony Search Metaheuristic Algorithm Pulse Emission Harmony Search Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Altringham, J.D.: Bats: Biology and Behaviour. Oxford Univesity Press, Oxford (1996)Google Scholar
  2. 2.
    Colin, T.: The Varienty of Life. Oxford University Press, Oxford (2000)Google Scholar
  3. 3.
    Deep, K., Bansal, J.C.: Mean particle swarm optimisation for function optimisation. Int. J. Comput. Intel. Studies 1, 72–92 (2009)Google Scholar
  4. 4.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: Harmony search. Simulation 76, 60–68 (2001)CrossRefGoogle Scholar
  5. 5.
    Holland, J.H.: Adapation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  6. 6.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, pp. 1942–1945 (1995)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Swarm Intelligence. Academic Press, London (2001)Google Scholar
  8. 8.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Liang, J.J., Suganthan, P.N., Deb, K.: Novel composition test functions for numerical global optimization. In: Proc. IEEE Int. Swarm Intel. Symp., pp. 68–75 (2005)Google Scholar
  10. 10.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  11. 11.
    Richardson, P.: Bats. Natural History Museum, London (2008)Google Scholar
  12. 12.
    Richardson, P.: The secrete life of bats,
  13. 13.
    Yang, X.-S.: Nature-inspired Metaheuristic Algorithms. Luniver Press (2008)Google Scholar
  14. 14.
    Yang, X.-S.: Harmony search as a metaheuristic algorithm. In: Geem, Z.W. (ed.) Music-Inspired Harmony Search Algorithm: Theory and Applications, pp. 1–14. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations