Английская Википедия:Bat algorithm

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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 <math>v_i</math> at position (solution) <math>x_i</math> with a varying frequency or wavelength and loudness <math>A_i</math>. As it searches and finds its prey, it changes frequency, loudness and pulse emission rate <math>r</math>. 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

Шаблон:Reflist

Further reading

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

Шаблон:Optimization algorithms Шаблон:Swarming

  1. J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).
  2. P. Richardson, Bats. Natural History Museum, London, (2008)
  3. Шаблон:Cite journal
  4. Yang, X. S., Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).
  5. Шаблон:Cite journal
  6. Шаблон:Cite journal