Bat algorithm

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Bat-inspired algorithm is a metaheuristic optimization algorithm developed by Xin-She Yang in 2010.[1] This bat algorithm is based on the echolocation behaviour of microbats with varying pulse rates of emission and loudness.[2][3]

Algorithm Description[edit]

The idealization of the echolocation of microbats can be summarized as follows: Each virtual bat flies randomly with a velocity v_i at position (solution) x_i with a varying frequency or wavelength and loudness A_i. As it searches and finds its prey, it changes frequency, loudness and pulse emission rate r. 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/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]

A Matlab demo is available at the Matlab exchange[7]

Multi-objective Bat Algorithm (MOBA)[edit]

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.[8] Another multiobjective bat algorithm by combining bat algorithm with NSGA-II produces very competitive results with good efficiency.[9]

Bat Algorithm Embedded with FLANN (BAT-FLANN)[edit]

BAT-FLANN model was proposed by Sashikala et al. in 2012.[10] 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)[edit]

Binary Bat Algorithm was proposed by Mirjalili et al. in 2014.[11] A V-shaped transfer function [12] was employed to allow BBA to solve binary problems.

Applications[edit]

Bat algorithm has been used for engineering design,[13] classifications of gene expression data is done by BAT-FLANN model by Sashikala Mishra,kailash shaw and Debahuti Mishra.[14] A fuzzy bat clustering method has been developed to solve ergonomic workplace problems[15] An interesting approach using fuzzy systems and bat algorithm has shown a reliable match between prediction and actual data for exergy modelling.[16]

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.[17]

References[edit]

  1. ^ X. S. Yang, 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
  2. ^ J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).
  3. ^ P. Richardson, Bats. Natural History Museum, London, (2008)
  4. ^ Yang, X. S., Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).
  5. ^ Parpinelli, R. S., and Lopes, H. S., New inspirations in swarm intelligence: a survey,Int. J. Bio-Inspired Computation, Vol. 3, 1-16 (2011).
  6. ^ 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).
  7. ^ here http://www.mathworks.com/matlabcentral/fileexchange/37582
  8. ^ X. S. Yang, bat algorithm for multi-objective optimisation, Int. J. Bio-Inspired Computation, Vol. 3, 267-274 (2011).
  9. ^ 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).
  10. ^ S. Mishra, K. Shaw, D. Mishra, A new metaheuristic classification approach for micro array data,Procedia Technology, Vol. 4, pp. 802-806 (2012).
  11. ^ 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
  12. ^ 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
  13. ^ 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).
  14. ^ S. Mishra, K. Shaw, D. Mishra, A new metaheuristic classification approach for microarray data,Procedia Technology, Vol. 4, pp. 802-806 (2012).
  15. ^ 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).
  16. ^ 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).
  17. ^ 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).