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The '''Bat-inspired algorithm''' is a [[metaheuristic]] searchalgorithm 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) (Eds.| J.arxiv R= 1004.4170| Gonzalezjournal et= alStudies in Computational Intelligence | volume = 284 | pages = 65–74 | bibcode = 2010arXiv1004.),4170Y Studies}}</ref>
in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010). http://arxiv.org/abs/1004.4170 </ref> This bat algorithm is based on the echolocation behaviour of [[microbats]] with varying pulse rates of emission and loudness.<ref>Altringham, J. D., Bats: Biology and Behaviour, Oxford Univesity Press, (1996).</ref><ref>Richardson, P., Bats. Natural History Museum, London, (2008)</ref>
== Algorithm Description ==
== Metaphor ==
The idealization of [echolocation]] 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
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?id=iVB_ETlh4ogC&q=bat+algorithm&pg=PR5 Nature-Inspired Metaheuristic AlgoirthmsAlgorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in Matlab[[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal | last1 = Parpinelli, | first1 = R. S., and| last2 = Lopes, | first2 = H. S., | s2cid = 16866891 | year = 2011 | title = New inspirations in swarm intelligence: aA survey,Int.| J.journal = International Journal of Bio-Inspired Computation,| Vol.volume = 3, 1-16| (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 ==
== Multi-objective Bat Algorithm (MOBA) ==
* [[List of metaphor-based metaheuristics]]
Using a simple weighted sum with random weights, a very effective but yet simple multiobjective bat algorithm (MOBA) has been developed to solve multiobjective engineering design tasks.<ref>X. S. Yang, bat algorithm for multi-objective optimisation, Int. J. Bio-Inspired Computation, Vol. 3, 267-274 (2011). </ref>
== Applications References==
A fuzzy bat clustering method has been developed to solve ergonomic workplace problems<ref>Khan, K., Nikov, A., Sahai A., A Fuzzy Bat Clustering Method for Ergonomic Screening of Office Workplaces,S3T 2011,
Advances in Intelligent and Soft Computing, 2011, Volume 101/2011, 59-66 (2011).</ref>
== ReferencesFurther reading ==
*Yang, X.-S. (2014), ''Nature-Inspired Optimization Algorithms'', [[Elsevier]].
[[Category:Heuristic algorithms]]
{{Optimization algorithms}}
{{swarming}}
[[Category:Nature-inspired metaheuristics]]
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