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Bat-inspired algorithm is a [[metaheuristic]] [[optimization]] algorithm developed by Xin-She Yang in 2010.<ref>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</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>


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== Algorithm Description ==
 
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 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> 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>
 
== 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>
 
== 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.<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>
 
== References ==
{{Reflist|33em}}
 
{{swarming}}
 
[[Category:Heuristic algorithms]]
[[Category:Evolutionary algorithms]]

Revision as of 09:37, 8 November 2013

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

The idealization of the echolocation of microbats can be summarized as follows: Each virtual bat flies randomly with a velocity vi at position (solution) xi with a varying frequency or wavelength and loudness Ai. 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)

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]

Applications

Bat algorithm has been used for engineering design,[10] classifications.[11] A fuzzy bat clustering method has been developed to solve ergonomic workplace problems[12] An interesting approach using fuzzy systems and bat algorithm has shown a reliable match between prediction and actual data for exergy modelling.[13]

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.[14]

References

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Template:Swarming

  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 Algoirthms, 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. ↑ 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).
  11. ↑ S. Mishra, K. Shaw, D. Mishra, A new metaheuristic classification approach for microarray data,Procedia Technology, Vol. 4, pp. 802-806 (2012).
  12. ↑ 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).
  13. ↑ 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).
  14. ↑ 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).
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