Bat algorithm - Revision history

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	*Yang, X.-S. (2014), ''Nature-Inspired Optimization Algorithms'', [[Elsevier]].		*Yang, X.-S. (2014), ''Nature-Inspired Optimization Algorithms'', [[Elsevier]].

			{{Optimization algorithms}}
	{{swarming}}		{{swarming}}

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	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.		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., [https://books.google.com/books?~~hl=en&lr=&~~id=iVB_ETlh4ogC&oi=~~fnd~~&pg=PR5~~&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false~~ Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{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}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 }}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., [https://books.google.com/books?id=iVB_ETlh4ogC&q=bat+algorithm&pg=PR5 Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{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}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 }}</ref>

	== See also ==		== See also ==

Pikamander2 at 06:32, 15 August 2022

2022-08-15T06:32:44Z

← Previous revision		Revision as of 06:32, 15 August 2022
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	== See also ==		== See also ==
	[[List of metaphor-based metaheuristics]]		* [[List of metaphor-based metaheuristics]]

	==References==		==References==

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2020-12-14T21:58:59Z

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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.<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>{{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 ~~\| issue =~~ \| pages = 65–74 \| bibcode = 2010arXiv1004.4170Y }}</ref>		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.<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>{{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 }}</ref>

	== Metaphor ==		== Metaphor ==
	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.		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., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{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 ~~\| issue =~~ \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 ~~\| url =~~ \| journal = Applied Mechanics and Materials \| volume = 148-149 ~~\| issue =~~ \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{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}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 }}</ref>

	== See also ==		== See also ==

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← Previous revision		Revision as of 23:50, 4 June 2020
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	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.		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., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: A survey~~\| url = https://semanticscholar.org/paper/4548c49d7e75f7bac860d2aea3ac7445056d3178~~\| journal = International Journal of Bio-Inspired Computation\| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{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 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>

	== See also ==		== See also ==

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2019-11-24T06:34:57Z

← Previous revision		Revision as of 06:34, 24 November 2019
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	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.		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., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey~~,Int~~ \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: A survey\| url = https://semanticscholar.org/paper/4548c49d7e75f7bac860d2aea3ac7445056d3178\| journal = International Journal of Bio-Inspired Computation\| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>

	== See also ==		== See also ==

Jarble: linking

2019-06-13T23:43:33Z

linking

← Previous revision		Revision as of 23:43, 13 June 2019
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	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.		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 Algorithms, 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., [https://books.google.com/books?hl=en&lr=&id=iVB_ETlh4ogC&oi=fnd&pg=PR5&dq=%22Nature-Inspired+Metaheuristic+Algorithms,+2nd+Edition%22+luniver&ots=DwgtqhEKua&sig=bcpfrzMR691SLIzIUIiA0GkJdHo#v=onepage&q=bat%20algorithm&f=false Nature-Inspired Metaheuristic Algorithms], 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>

	== See also ==		== See also ==

Daviddwd: /* Metaphor */

2018-10-09T05:07:37Z

Metaphor

← Previous revision		Revision as of 05:07, 9 October 2018
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	A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>

	==~~Notes~~==		== See also ==
			[[List of metaphor-based metaheuristics]]

			==References==
	{{Reflist\|33em}}		{{Reflist\|33em}}

Daviddwd: /* Metaphor */ links

2018-10-09T05:07:14Z

Metaphor: links

← Previous revision		Revision as of 05:07, 9 October 2018
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	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.		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 Algorithms, 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>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).</ref> where a demo program in [[MATLAB]]/[[GNU Octave]] is available, while a comprehensive review is carried out by Parpinelli and Lopes.<ref>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>

	==Notes==		==Notes==

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2018-06-19T03:46:29Z

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← Previous revision		Revision as of 03:46, 19 June 2018
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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.<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>{{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 \| issue = \| pages = 65–74 }}</ref>		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.<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>{{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 \| issue = \| pages = 65–74 \| bibcode = 2010arXiv1004.4170Y }}</ref>

	== Metaphor ==		== Metaphor ==
	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.		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 Algorithms, 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>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134}}</ref>		A detailed introduction of metaheuristic algorithms including the bat algorithm is given by Yang<ref>Yang, X. S., Nature-Inspired Metaheuristic Algorithms, 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>{{cite journal \| last1 = Parpinelli \| first1 = R. S. \| last2 = Lopes \| first2 = H. S. \| year = 2011 \| title = New inspirations in swarm intelligence: a survey,Int \| url = \| journal = J. Bio-Inspired Computation \| volume = 3 \| issue = \| pages = 1–16 \| doi=10.1504/ijbic.2011.038700}}</ref> A further improvement is the development of an evolving bat algorithm (EBA) with better efficiency.<ref>{{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 \| url = \| journal = Applied Mechanics and Materials \| volume = 148-149 \| issue = \| pages = 134–137 \| doi=10.4028/www.scientific.net/amm.148-149.134\| bibcode = 2011AMM...148..134T }}</ref>

	==Notes==		==Notes==