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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.<ref>J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).</ref><ref>P. Richardson, Bats. Natural History Museum, London, (2008)</ref> The Bat algorithm was developed by [[Xin-She Yang]] in 2010.<ref>{{cite journal | last1 = Yang | first1 = X. S. | year = 2010 | title = A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) | arxiv = 1004.4170| journal = Studies in Computational Intelligence | volume = 284 | issue = | pages = 65–74 | bibcode = 2010arXiv1004.4170Y }}</ref>
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.<ref>J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).</ref><ref>P. Richardson, Bats. Natural History Museum, London, (2008)</ref> The Bat algorithm was developed by [[Xin-She Yang]] in 2010.<ref>{{cite journal | last1 = Yang | first1 = X. S. | year = 2010 | title = A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) | arxiv = 1004.4170| journal = Studies in Computational Intelligence | volume = 284 | pages = 65–74 | bibcode = 2010arXiv1004.4170Y }}</ref>


== Metaphor ==
== Metaphor ==
The idealization of the [[Animal echolocation|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.
The idealization of the [[Animal echolocation|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<ref>Yang, X. S., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal | last1 = Parpinelli | first1 = R. S. | last2 = Lopes | first2 = H. S. | s2cid = 16866891 | year = 2011 | title = New inspirations in swarm intelligence: A survey| journal = International Journal of Bio-Inspired Computation| volume = 3 | issue = | pages = 1–16 | doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{cite journal | last1 = Tsai | first1 = P. W. | last2 = Pan | first2 = J. S. | last3 = Liao | first3 = B. Y. | last4 = Tsai | first4 = M. J. | last5 = Istanda | first5 = V. | year = 2012 | title = Bat algorithm inspired algorithm for solving numerical optimization problems | url = | journal = Applied Mechanics and Materials | volume = 148-149 | issue = | pages = 134–137 | doi=10.4028/www.scientific.net/amm.148-149.134| bibcode = 2011AMM...148..134T }}</ref>
A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal | last1 = Parpinelli | first1 = R. S. | last2 = Lopes | first2 = H. S. | s2cid = 16866891 | year = 2011 | title = New inspirations in swarm intelligence: A survey| journal = International Journal of Bio-Inspired Computation| volume = 3 | pages = 1–16 | doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{cite journal | last1 = Tsai | first1 = P. W. | last2 = Pan | first2 = J. S. | last3 = Liao | first3 = B. Y. | last4 = Tsai | first4 = M. J. | last5 = Istanda | first5 = V. | year = 2012 | title = Bat algorithm inspired algorithm for solving numerical optimization problems | journal = Applied Mechanics and Materials | volume = 148-149 | pages = 134–137 | doi=10.4028/www.scientific.net/amm.148-149.134| bibcode = 2011AMM...148..134T }}</ref>


== See also ==
== See also ==

Revision as of 21:58, 14 December 2020

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

List of metaphor-based metaheuristics

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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