Bat algorithm: Difference between revisions

Revision as of 10:04, 15 January 2016

Revision as of 10:14, 15 January 2016

Line 1:

The '''Bat-inspired 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.<ref>J. D. Altringham, Bats: Biology and Behaviour, Oxford University Press, (1996).</ref><ref>P. Richardson, Bats. Natural History Museum, London, (2008)</ref> The Bat algorithm was 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>

== Algorithm Description ==

== 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.