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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., Nature-Inspired Metaheuristic Algorithms, 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> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>P. W. Tsai, J. S. Pan, B. Y. Liao, M. J. Tsai, V. Istanda, Bat algorithm inspired algorithm for solving numerical optimization problems, Applied Mechanics and Materials, Vo.. 148-149, pp.134-137 (2012).</ref>
A Matlab demo is available at the Matlab exchange<ref>here http://www.mathworks.com/matlabcentral/fileexchange/37582</ref>
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== 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 problems, ''IEEE Trans. Magnetics'', Vol. 48 (2), 947-950 (2012).</ref>
== Bat Algorithm Embedded with FLANN (BAT-FLANN) ==
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.
The same model has been used for designing a Meta-heuristic Framework for Secondary Protein Structure Prediction where BAT has been used to optimize FLANN network.<ref>k. Shaw, D. Mishra,A Meta-heuristic Framework for Secondary Protein Structure Prediction using BAT-FLANN Optimization Algorithm, ''Indian Journal of Science and Technology'', Vol 8(16),pp. 951-960 (2015).</ref>
== Directed Artificial Bat Algorithm (DABA) ==
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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|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|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> It also helps in the field of Protein Secondary Structure Prediction by means of optimizing classifier [11].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
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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
==Notes==
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