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Bat-inspired algorithm is a [[metaheuristic]] search [[optimization]] developed by Xin-She Yang in 2010.<ref>Yang, X.-S., A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies
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 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 ==
The idealization of [[echolocation]] can be summarized as follows: Each virtual bat fly 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.
A detailed intrudction of metaheuristic algorithms including bat algorithms is given by Yang <ref>Yang, X. S., Nature-Inspired Metaheuristic Algoirthms, 2nd Edition, Luniver Press, (2010).</ref> where a demo program in Matlab/Octave is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>Parpinelli, R. S., and Lopes, H. S., New inspirations in swarm intelligence: a survey,Int. J. Bio-Inspired Computation, Vol. 3, 1-16 (2011).<ref>
== Applications ==
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>
== References ==
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