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The '''Bat-inspired 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. Yang,| 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>J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).</ref><ref>P. Richardson, Bats. Natural History Museum, London, (2008)</ref>
== Algorithm Description ==
== 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.
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. Tsai,| last2 = Pan | first2 = J. S. Pan,| last3 = Liao | first3 = B. Y. Liao,| last4 = Tsai | first4 = M. J. Tsai,| last5 = Istanda | first5 = V. Istanda,| year = 2012 | title = Bat algorithm inspired algorithm for solving numerical optimization problems, | journal = Applied Mechanics and Materials, Vo..| volume = 148-149, pp| pages = 134–137 | doi=10.1344028/www.scientific.net/amm.148-137149.134| (2012)bibcode = 2011AMM...148..134T }}</ref>
== See also ==
A Matlab demo is available at the Matlab exchange<ref>here http://www.mathworks.com/matlabcentral/fileexchange/37582</ref>
* [[List of metaphor-based metaheuristics]]
==References==
== Multi-objective Bat Algorithm (MOBA) ==
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> Another multiobjective bat algorithm by combining bat algorithm with
NSGA-II produces very competitive results with good efficiency.<ref>T. C. Bora, L. S. Coelho, L. Lebensztajn, Bat-inspired optimization
approach for the brushless DC wheel motor problem, IEEE Trans. Magnetics, Vol. 48 (2), 947-950 (2012).</ref>
== Further reading ==
== Bat Algorithm Embedded with FLANN (BAT-FLANN) ==
*Yang, X.-S. (2014), ''Nature-Inspired Optimization Algorithms'', [[Elsevier]].
BAT-FLANN model was proposed by Sashikala et al. in 2012.<ref>S. Mishra, K. Shaw, D. Mishra, A new metaheuristic classification approach for micro array data,Procedia Technology, Vol. 4, pp. 802-806 (2012).</ref> to solve classification of gene expression data. Using simple bat frequency,loudness and pulse updation logic and random weight, a very effective algorithm is designed that give promising result.
== Binary Bat Algorithm (BBA) ==
Binary Bat Algorithm was proposed by Mirjalili et al. in 2014.<ref>
S. Mirjalili, S. M. Mirjalili, X. Yang, Binary Bat Algorithm, Neural Computing and Applications, In press, 2014, Springer DOI: http://dx.doi.org/10.1007/s00521-013-1525-5</ref> A V-shaped transfer function <ref>S. Mirjalili, A. Lewis, S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization, Swarm and Evolutionary Computation, Volume 9, April 2013, Pages 1-14, DOI: http://dx.doi.org/10.1016/j.swevo.2012.09.002</ref> was employed to allow BBA to solve binary problems.
== Applications ==
Bat algorithm has been used for engineering design,<ref>X. S. Yang and A. H. Gandomi, Bat algorithm: a novel approach for global engineering optimization, Engineering Computations, Vol. 29, No. 5, pp. 464-483 (2012).</ref> classifications of gene expression data is done by BAT-FLANN model by Sashikala Mishra,kailash shaw and Debahuti Mishra.<ref>S. Mishra, K. Shaw, D. Mishra, A new metaheuristic classification approach for microarray data,Procedia Technology, Vol. 4, pp. 802-806 (2012).</ref>
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>
An interesting approach using fuzzy systems and bat algorithm has shown
a reliable match between prediction and actual data for exergy modelling.<ref>T. A. Lemma, Use of fuzzy systems and bat algorithm for exergy modelling in a gas turbine generator, IEEE Colloquium on Humanities, Science and Engineering (CHUSER'2011), pp. 305-310 (2011).</ref>
A detailed comparison of bat algorithm (BA) with genetic algorithm (GA), PSO and other methods for training feed forward neural networks concluded clearly that BA has advantages over other algorithms.<ref>K. Khan and A. Sahai, A comparison of BA, GA, PSO, BP and LM for
training feed forward neural networks in e-learning context, Int. J. Intelligent Systems and Applications (IJISA), Vol. 4, No. 7, pp. 23-29 (2012).</ref>
<ref>Bat algorithm based Soft computing Approach to Perceive Hairline Bone Fracture in Medical X-ray Images”, Goutam Das / International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 4 No. 04 Apr 2013,pp 432-436.</ref>== References ==
{{Optimization algorithms}}
{{swarming}}
[[Category:HeuristicNature-inspired algorithmsmetaheuristics]]
[[Category:Evolutionary algorithms]]
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