The Bat algorithm is a metaheuristic algorithm for global optimization. It was inspired by the echolocation behaviour of microbats, with varying pulse rates of emission and loudness.[1][2] The Bat algorithm was developed by Xin-She Yang in 2010.[3]

Metaphor

The idealization of the echolocation of microbats can be summarized as follows: Each virtual bat flies randomly with a velocity at position (solution) with a varying frequency or wavelength and loudness . As it searches and finds its prey, it changes frequency, loudness and pulse emission rate . Search is intensified by a local random walk. Selection of the best continues until certain stop criteria are met. This essentially uses a frequency-tuning technique to control the dynamic behaviour of a swarm of bats, and the balance between exploration and exploitation can be controlled by tuning algorithm-dependent parameters in bat algorithm.

A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang[4] where a demo program in MATLAB/GNU Octave is available, while a comprehensive review is carried out by Parpinelli and Lopes.[5] A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.[6]

See also

References

  1. ↑ J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).
  2. ↑ P. Richardson, Bats. Natural History Museum, London, (2008)
  3. ↑ Yang, X. S. (2010). "A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010)". Studies in Computational Intelligence. 284: 65–74. arXiv:1004.4170. Bibcode:2010arXiv1004.4170Y.
  4. ↑ Yang, X. S., Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).
  5. ↑ Parpinelli, R. S.; Lopes, H. S. (2011). "New inspirations in swarm intelligence: A survey". International Journal of Bio-Inspired Computation. 3: 1–16. doi:10.1504/ijbic.2011.038700. S2CID 16866891.
  6. ↑ Tsai, P. W.; Pan, J. S.; Liao, B. Y.; Tsai, M. J.; Istanda, V. (2012). "Bat algorithm inspired algorithm for solving numerical optimization problems". Applied Mechanics and Materials. 148–149: 134–137. Bibcode:2011AMM...148..134T. doi:10.4028/www.scientific.net/amm.148-149.134.

Further reading

  • Yang, X.-S. (2014), Nature-Inspired Optimization Algorithms, Elsevier.
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