Bat algorithm

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The Bat 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.[1][2] The Bat algorithm was developed by Xin-She Yang in 2010.[3]

Algorithm Description

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]

Implementations

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]

Bat Algorithm Embedded with FLANN (BAT-FLANN)

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. 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.[11]

Directed Artificial Bat Algorithm (DABA)

Directed Artificial Bat Algorithm was proposed by Rekaby in Aug 2013.[12] This algorithm is simulating the echo system of that bats, and how they use this system in prey finding and obstacle avoidance. In this research, it proved the efficiency of DABA algorithm comparing the results with ABC Algorithm.

Binary Bat Algorithm (BBA)

Binary Bat Algorithm was proposed by Mirjalili et al. in 2014.[13] A V-shaped transfer function [14] was employed to allow BBA to solve binary problems.

Applications

Bat algorithm has been used for engineering design,[15] classifications of gene expression data is done by BAT-FLANN model by Sashikala Mishra,kailash shaw and Debahuti Mishra.,[16] 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[17] An interesting approach using fuzzy systems and bat algorithm has shown a reliable match between prediction and actual data for exergy modelling.[18]

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.[19]

Notes

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Further reading

  • Kotteeswaran, R. & Sivakumar, L. (2013),"A Novel Bat Algorithm Based Re-Tuning of PI Controller of Coal Gasifier for Optimum Response", Mining Intelligence and Knowledge Exploration Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (LNAI), vol. 8284, pp. 506–517, Springer.
  • Kotteeswaran, R. & Sivakumar, L. (2013),"Optimal Partial-retuning of decentralised PI controller of coal gasifier using Bat Algorithm", Swarm, Evolutionary, and Memetic Computing Lecture Notes in Computer Science (LNCS), vol. 8297, pp. 750–761, Springer.
  • Yang, X.-S. (2014), Nature-Inspired Optimization Algorithms, Elsevier.
  1. ↑ J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).
  2. ↑ P. Richardson, Bats. Natural History Museum, London, (2008)
  3. ↑ 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
  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 problems, 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. ↑ 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).
  12. ↑ A. Rekaby, "Directed Artificial Bat Algorithm (DABA): A New Bio-Inspired Algorithm," in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Mysore, 2013.
  13. ↑ 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
  14. ↑ 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
  15. ↑ 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).
  16. ↑ S. Mishra, K. Shaw, D. Mishra, A new metaheuristic classification approach for microarray data,Procedia Technology, Vol. 4, pp. 802-806 (2012).
  17. ↑ 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).
  18. ↑ 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).
  19. ↑ 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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