Fireworks algorithm - Revision history

Jarble: adding Template:Optimization algorithms

2023-07-01T19:32:46Z

← Previous revision		Revision as of 19:32, 1 July 2023
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	== Algorithm Description ==		== Algorithm Description ==
	The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref> The algorithm begins with selecting <math>n</math> initial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to the optimal location. After each spark location is evaluated, the algorithm terminates if an optimal location was found, or it repeats with <math>n</math> new firework locations if an optimal location was not found.		The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence \|url=https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bf2c921d75705ba8e74df025953d7643cb8e0af4}}</ref> The algorithm begins with selecting <math>n</math> initial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to the optimal location. After each spark location is evaluated, the algorithm terminates if an optimal location was found, or it repeats with <math>n</math> new firework locations if an optimal location was not found.

	==References==		==References==
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	[[Category:Optimization algorithms and methods]]		[[Category:Optimization algorithms and methods]]
			{{Optimization algorithms}}

	{{algorithm-stub}}		{{algorithm-stub}}

Izno: /* Algorithm Description */ clean ref

2020-02-10T17:52:25Z

Algorithm Description: clean ref

← Previous revision		Revision as of 17:52, 10 February 2020
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	== Algorithm Description ==		== Algorithm Description ==
	The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y ~~\| display-authors = 2~~ \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref> The algorithm begins with selecting <math>n</math> initial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to the optimal location. After each spark location is evaluated, the algorithm terminates if an optimal location was found, or it repeats with <math>n</math> new firework locations if an optimal location was not found.		The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref> The algorithm begins with selecting <math>n</math> initial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to the optimal location. After each spark location is evaluated, the algorithm terminates if an optimal location was found, or it repeats with <math>n</math> new firework locations if an optimal location was not found.

	==References==		==References==

Cjp419: Moved the references from bottom of the page to a Reference section.

2020-02-08T21:41:32Z

Moved the references from bottom of the page to a Reference section.

← Previous revision		Revision as of 21:41, 8 February 2020
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	The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| display-authors = 2 \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref> The algorithm begins with selecting <math>n</math> initial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to the optimal location. After each spark location is evaluated, the algorithm terminates if an optimal location was found, or it repeats with <math>n</math> new firework locations if an optimal location was not found.		The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| display-authors = 2 \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref> The algorithm begins with selecting <math>n</math> initial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to the optimal location. After each spark location is evaluated, the algorithm terminates if an optimal location was found, or it repeats with <math>n</math> new firework locations if an optimal location was not found.

			==References==
			{{reflist}}

	==External links==		==External links==

Cjp419: Described overview of the algorithm.

2020-02-08T21:30:17Z

Described overview of the algorithm.

← Previous revision		Revision as of 21:30, 8 February 2020
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	== Algorithm Description ==		== Algorithm Description ==
	The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| display-authors = 2 \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref>		The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| display-authors = 2 \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref> The algorithm begins with selecting <math>n</math> initial locations where the fireworks will explode. Then the amount of sparks and their locations are determined based on the proximity of the firework to the optimal location. After each spark location is evaluated, the algorithm terminates if an optimal location was found, or it repeats with <math>n</math> new firework locations if an optimal location was not found.

Cjp419: Added a new algorithm description section, a brief elaboration on the summary, and a reference to the paper.

2020-02-08T21:20:49Z

Added a new algorithm description section, a brief elaboration on the summary, and a reference to the paper.

← Previous revision		Revision as of 21:20, 8 February 2020
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	The '''Fireworks Algorithm (FWA)''' is a [[swarm intelligence]] algorithm that explores a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.		The '''Fireworks Algorithm (FWA)''' is a [[swarm intelligence]] algorithm that explores a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.

			== Algorithm Description ==
			The algorithm is implemented and described in terms of the explosion process of fireworks: explosions occur at specific points, and "sparks" fan out from the explosion. Each spark location is considered until an adequately optimal point is found. In terms of optimization, when finding an <math>x_j</math> satisfying <math>f(x_j)=y</math>, the algorithm continues until a spark is sufficiently near <math>x_j</math>.<ref>{{Citation \| last1 = Tan \| first1 = T \| last2 = Zhu \| first2 = Y \| display-authors = 2 \| title = Fireworks algorithm for optimization \| year = 2010 \| publisher = International Conference in Swarm Intelligence }}</ref>


	==External links==		==External links==

Cjp419: Reference and link swarm intelligence for context and to clarify "approach"

2020-02-01T23:53:31Z

Reference and link swarm intelligence for context and to clarify "approach"

← Previous revision		Revision as of 23:53, 1 February 2020
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	The '''Fireworks Algorithm (FWA)''' is an ~~approach~~ to ~~exploring~~ a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.		The '''Fireworks Algorithm (FWA)''' is a [[swarm intelligence]] algorithm that explores a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.

	==External links==		==External links==

141.71.163.33: remove dead external links and change them to appropriate links

2019-06-13T10:32:02Z

remove dead external links and change them to appropriate links

← Previous revision		Revision as of 10:32, 13 June 2019
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	==External links==		==External links==
	* [http://www.cil.pku.edu.cn/research/fa/ ~~http://www.cil.pku.edu.cn/research/fa/~~]		* [http://www.cil.pku.edu.cn/research/swgence/271228.htm Fireworks Algorithm]
	* [http://www.cil.pku.edu.cn~~/research/fwa/index.html~~]		* [http://www.cil.pku.edu.cn Computational Intelligence Laboratory, Peking University]
	* [http://www.cil.pku.edu.cn]

	[[Category:Optimization algorithms and methods]]		[[Category:Optimization algorithms and methods]]

65.49.199.23: /* External links */

2019-01-12T03:29:52Z

External links

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	==External links==		==External links==
	* [http://www.cil.pku.edu.cn/research/fa/ http://www.cil.pku.edu.cn/research/fa/]		* [http://www.cil.pku.edu.cn/research/fa/ http://www.cil.pku.edu.cn/research/fa/]
			* [http://www.cil.pku.edu.cn/research/fwa/index.html]
			* [http://www.cil.pku.edu.cn]

	[[Category:Optimization algorithms and methods]]		[[Category:Optimization algorithms and methods]]

121.200.54.2 at 06:15, 31 December 2018

2018-12-31T06:15:22Z

← Previous revision		Revision as of 06:15, 31 December 2018
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	The '''Fireworks Algorithm''' is an approach to exploring a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.		The '''Fireworks Algorithm (FWA)''' is an approach to exploring a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.

	==External links==		==External links==

121.200.54.2 at 06:13, 31 December 2018

2018-12-31T06:13:42Z

← Previous revision		Revision as of 06:13, 31 December 2018
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	The '''Fireworks Algorithm''' ~~('''FWA''')~~ is an approach to exploring a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.		The '''Fireworks Algorithm''' is an approach to exploring a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.

	==External links==		==External links==