Bat algorithm

Bat-inspired algorithm is a metaheuristic search optimization 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 [echolocation]] 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.

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]

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.^[7]

Applications

A fuzzy bat clustering method has been developed to solve ergonomic workplace problems^[8]

References

^ Yang, X.-S., 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
^ Altringham, J. D., Bats: Biology and Behaviour, Oxford Univesity Press, (1996).
^ Richardson, P., Bats. Natural History Museum, London, (2008)
^ Yang, X. S., Nature-Inspired Metaheuristic Algoirthms, 2nd Edition, Luniver Press, (2010).
^ Parpinelli, R. S., and Lopes, H. S., New inspirations in swarm intelligence: a survey,Int. J. Bio-Inspired Computation, Vol. 3, 1-16 (2011).
^ 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).
^ X. S. Yang, bat algorithm for multi-objective optimisation, Int. J. Bio-Inspired Computation, Vol. 3, 267-274 (2011).
^ 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).

[1] Yang, X.-S., 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] Altringham, J. D., Bats: Biology and Behaviour, Oxford Univesity Press, (1996).

[3] Richardson, P., 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] X. S. Yang, bat algorithm for multi-objective optimisation, Int. J. Bio-Inspired Computation, Vol. 3, 267-274 (2011).

[8] 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).

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