bat algorithm
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.J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).P. Richardson, Bats. Natural History Museum, London, (2008) The Bat algorithm was developed by Xin-She Yang in 2010.{{cite journal | last1 = Yang | first1 = X. S. | year = 2010 | title = A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) | arxiv = 1004.4170| journal = Studies in Computational Intelligence | volume = 284 | pages = 65–74 | bibcode = 2010arXiv1004.4170Y }}
Metaphor
The idealization of the echolocation of microbats can be summarized as follows: Each virtual bat flies randomly with a velocity at position (solution) with a varying frequency or wavelength and loudness . As it searches and finds its prey, it changes frequency, loudness and pulse emission rate . 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 YangYang, X. S., [https://books.google.com/books?id=iVB_ETlh4ogC&q=bat+algorithm&pg=PR5 Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010). where a demo program in MATLAB/GNU Octave is available, while a comprehensive review is carried out by Parpinelli and Lopes.{{cite journal | last1 = Parpinelli | first1 = R. S. | last2 = Lopes | first2 = H. S. | s2cid = 16866891 | year = 2011 | title = New inspirations in swarm intelligence: A survey| journal = International Journal of Bio-Inspired Computation| volume = 3 | pages = 1–16 | doi=10.1504/ijbic.2011.038700}} A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.{{cite journal | last1 = Tsai | first1 = P. W. | last2 = Pan | first2 = J. S. | last3 = Liao | first3 = B. Y. | last4 = Tsai | first4 = M. J. | last5 = Istanda | first5 = V. | year = 2012 | title = Bat algorithm inspired algorithm for solving numerical optimization problems | journal = Applied Mechanics and Materials | volume = 148-149 | pages = 134–137 | doi=10.4028/www.scientific.net/amm.148-149.134| bibcode = 2011AMM...148..134T }}
See also
References
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Further reading
- Yang, X.-S. (2014), Nature-Inspired Optimization Algorithms, Elsevier.
{{Optimization algorithms}}
{{swarming}}