Bees algorithm - Revision history

Samuuurai: Added image and caption

2025-04-11T17:13:01Z

Added image and caption

← Previous revision		Revision as of 17:13, 11 April 2025
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	{{Short description\|Population-based search algorithm}}		{{Short description\|Population-based search algorithm}}
	{{distinguish\|Artificial bee colony algorithm}}		{{distinguish\|Artificial bee colony algorithm}}
			[[File:Honey_Bee_takes_Nectar.JPG \| thumb \| right \| The bee algorithm mimics the foraging behavior of honey bees.]]
	In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>		In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>

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← Previous revision		Revision as of 02:20, 12 January 2025
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	== Algorithm ==		== Algorithm ==
	The bees algorithm<ref name="Pham & Castellani, 2009"/><ref name="Pham & Ghanbarzadeh et. a. 2006">Pham D.T., Ghanbarzadeh A., Koc E., Otri S., Rahim S., Zaidi M., [https://www.academia.edu/8358231/The_Bees_Algorithm_A_Novel_Tool_for_Complex_Optimisation_Problems The Bees Algorithm, A Novel Tool for Complex Optimisation Problems]~~{{dead link\|date=July 2022\|bot=medic}}{{cbignore\|bot=medic}}~~, Proc 2nd Int Virtual Conf on Intelligent Production Machines and Systems (IPROMS 2006), Oxford: Elsevier, pp. 454-459, 2006.</ref> mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Each candidate solution is thought of as a food source (flower), and a population (colony) of ''n'' agents (bees) is used to search the solution space. Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness).		The bees algorithm<ref name="Pham & Castellani, 2009"/><ref name="Pham & Ghanbarzadeh et. a. 2006">Pham D.T., Ghanbarzadeh A., Koc E., Otri S., Rahim S., Zaidi M., [https://web.archive.org/web/20231226051053/https://www.academia.edu/8358231/The_Bees_Algorithm_A_Novel_Tool_for_Complex_Optimisation_Problems The Bees Algorithm, A Novel Tool for Complex Optimisation Problems], Proc 2nd Int Virtual Conf on Intelligent Production Machines and Systems (IPROMS 2006), Oxford: Elsevier, pp. 454-459, 2006.</ref> mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Each candidate solution is thought of as a food source (flower), and a population (colony) of ''n'' agents (bees) is used to search the solution space. Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness).

	The bees algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number ''T'' of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures: recruitment, local search, neighbourhood shrinking, site abandonment, and global search.		The bees algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number ''T'' of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures: recruitment, local search, neighbourhood shrinking, site abandonment, and global search.

MrOllie: Restored revision 1220736756 by Ajshul (talk): Rm citespam

2024-12-03T17:00:47Z

Restored revision 1220736756 by Ajshul (talk): Rm citespam

← Previous revision		Revision as of 17:00, 3 December 2024
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	== Variants ==		== Variants ==
	In addition to the basic bees algorithm,<ref name="Pham & Ghanbarzadeh et. a. 2006"/> there are a number of improved or hybrid versions of the BA, each of which focuses on some shortcomings of the basic BA. These variants include (but are not limited to):		In addition to the basic bees algorithm,<ref name="Pham & Ghanbarzadeh et. a. 2006"/> there are a number of improved or hybrid versions of the BA, each of which focuses on some shortcomings of the basic BA. These variants include (but are not limited to) fuzzy or enhanced BA (EBA),<ref name="Pham & Darwish 008">Pham D. T., Haj Darwish A., (2008), A. [https://www.researchgate.net/profile/Ahmed_Haj_Darwish/publication/321849894_Fuzzy_Selection_of_Local_Search_Sites_in_the_Bees_Algorithm/links/5a37c834aca272a6ec1e511a/Fuzzy-Selection-of-Local-Search-Sites-in-the-Bees-Algorithm.pdf Fuzzy Selection of Local Search Sites in the Bees Algorithm]. Proceedings of Innovative Production Machines and Systems (IPROMS 2008)</ref> grouped BA (GBA),<ref name="Nasrinpour & Massah & Teshnehlab 2017"/> hybrid modified BA (MBA)<ref name="Pham & Pham & Castellani 2011">Pham Q. T., Pham D. T., Castellani M., A modified Bees Algorithm and a statistics-based method for tuning its parameters. Proceedings of the Institution of Mechanical Engineers (ImechE), Part I: Journal of Systems and Control Eng., 2011 ({{doi\|10.1177/0959651811422759}})</ref> and so on.
		⚫	The pseudo-code for the '''grouped BA (GBA)''' <ref name="Nasrinpour & Massah & Teshnehlab 2017"/> is as follows.

	* fuzzy self-tuning BA (FST-BA) which introduces a set of fuzzy rules to autonomously compute parameters for each solution during the optimization process, thereby improving adaptability and convergence of the BA,<ref>{{Cite journal \|last=Zervoudakis \|first=Konstantinos \|last2=Tsafarakis \|first2=Stelios \|date=2024-12-01 \|title=Fuzzy Self-tuning Bees Algorithm for designing optimal product lines \|url=https://linkinghub.elsevier.com/retrieve/pii/S1568494624010020 \|journal=Applied Soft Computing \|volume=167 \|pages=112228 \|doi=10.1016/j.asoc.2024.112228 \|issn=1568-4946}}</ref>
	* fuzzy or enhanced BA (EBA),<ref name="Pham & Darwish 008">Pham D. T., Haj Darwish A., (2008), A. [https://www.researchgate.net/profile/Ahmed_Haj_Darwish/publication/321849894_Fuzzy_Selection_of_Local_Search_Sites_in_the_Bees_Algorithm/links/5a37c834aca272a6ec1e511a/Fuzzy-Selection-of-Local-Search-Sites-in-the-Bees-Algorithm.pdf Fuzzy Selection of Local Search Sites in the Bees Algorithm]. Proceedings of Innovative Production Machines and Systems (IPROMS 2008)</ref>
	* grouped BA (GBA),<ref name="Nasrinpour & Massah & Teshnehlab 2017" />
	* hybrid modified BA (MBA)<ref name="Pham & Pham & Castellani 2011">Pham Q. T., Pham D. T., Castellani M., A modified Bees Algorithm and a statistics-based method for tuning its parameters. Proceedings of the Institution of Mechanical Engineers (ImechE), Part I: Journal of Systems and Control Eng., 2011 ({{doi\|10.1177/0959651811422759}})</ref> and many others.

⚫	The pseudo-code for the '''grouped BA (GBA)''' <ref name="Nasrinpour & Massah & Teshnehlab 2017" /> is as follows.

	<syntaxhighlight lang="matlab">		<syntaxhighlight lang="matlab">

147.27.11.34: /* Variants */ added the fuzzy self tuning bees algorithm

2024-11-08T17:00:30Z

Variants: added the fuzzy self tuning bees algorithm

← Previous revision		Revision as of 17:00, 8 November 2024
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	== Variants ==		== Variants ==
	In addition to the basic bees algorithm,<ref name="Pham & Ghanbarzadeh et. a. 2006"/> there are a number of improved or hybrid versions of the BA, each of which focuses on some shortcomings of the basic BA. These variants include (but are not limited to) fuzzy or enhanced BA (EBA),<ref name="Pham & Darwish 008">Pham D. T., Haj Darwish A., (2008), A. [https://www.researchgate.net/profile/Ahmed_Haj_Darwish/publication/321849894_Fuzzy_Selection_of_Local_Search_Sites_in_the_Bees_Algorithm/links/5a37c834aca272a6ec1e511a/Fuzzy-Selection-of-Local-Search-Sites-in-the-Bees-Algorithm.pdf Fuzzy Selection of Local Search Sites in the Bees Algorithm]. Proceedings of Innovative Production Machines and Systems (IPROMS 2008)</ref> grouped BA (GBA),<ref name="Nasrinpour & Massah & Teshnehlab 2017"/> hybrid modified BA (MBA)<ref name="Pham & Pham & Castellani 2011">Pham Q. T., Pham D. T., Castellani M., A modified Bees Algorithm and a statistics-based method for tuning its parameters. Proceedings of the Institution of Mechanical Engineers (ImechE), Part I: Journal of Systems and Control Eng., 2011 ({{doi\|10.1177/0959651811422759}})</ref> and so on.		In addition to the basic bees algorithm,<ref name="Pham & Ghanbarzadeh et. a. 2006"/> there are a number of improved or hybrid versions of the BA, each of which focuses on some shortcomings of the basic BA. These variants include (but are not limited to):

			* fuzzy self-tuning BA (FST-BA) which introduces a set of fuzzy rules to autonomously compute parameters for each solution during the optimization process, thereby improving adaptability and convergence of the BA,<ref>{{Cite journal \|last=Zervoudakis \|first=Konstantinos \|last2=Tsafarakis \|first2=Stelios \|date=2024-12-01 \|title=Fuzzy Self-tuning Bees Algorithm for designing optimal product lines \|url=https://linkinghub.elsevier.com/retrieve/pii/S1568494624010020 \|journal=Applied Soft Computing \|volume=167 \|pages=112228 \|doi=10.1016/j.asoc.2024.112228 \|issn=1568-4946}}</ref>
			* fuzzy or enhanced BA (EBA),<ref name="Pham & Darwish 008">Pham D. T., Haj Darwish A., (2008), A. [https://www.researchgate.net/profile/Ahmed_Haj_Darwish/publication/321849894_Fuzzy_Selection_of_Local_Search_Sites_in_the_Bees_Algorithm/links/5a37c834aca272a6ec1e511a/Fuzzy-Selection-of-Local-Search-Sites-in-the-Bees-Algorithm.pdf Fuzzy Selection of Local Search Sites in the Bees Algorithm]. Proceedings of Innovative Production Machines and Systems (IPROMS 2008)</ref>
			* grouped BA (GBA),<ref name="Nasrinpour & Massah & Teshnehlab 2017" />
			* hybrid modified BA (MBA)<ref name="Pham & Pham & Castellani 2011">Pham Q. T., Pham D. T., Castellani M., A modified Bees Algorithm and a statistics-based method for tuning its parameters. Proceedings of the Institution of Mechanical Engineers (ImechE), Part I: Journal of Systems and Control Eng., 2011 ({{doi\|10.1177/0959651811422759}})</ref> and many others.

	The pseudo-code for the '''grouped BA (GBA)''' <ref name="Nasrinpour & Massah & Teshnehlab 2017"/> is as follows.		The pseudo-code for the '''grouped BA (GBA)''' <ref name="Nasrinpour & Massah & Teshnehlab 2017" /> is as follows.

	<syntaxhighlight lang="matlab">		<syntaxhighlight lang="matlab">

Ajshul: Adding short description: "Population-based search algorithm"

2024-04-25T16:18:38Z

Adding short description: "Population-based search algorithm"

← Previous revision		Revision as of 16:18, 25 April 2024
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			{{Short description\|Population-based search algorithm}}
	{{distinguish\|Artificial bee colony algorithm}}		{{distinguish\|Artificial bee colony algorithm}}
	In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>		In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>

RajeshAdwani: I have just updated the dead link with the relevant link

2023-12-26T05:10:51Z

I have just updated the dead link with the relevant link

← Previous revision		Revision as of 05:10, 26 December 2023
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	== Algorithm ==		== Algorithm ==
	The bees algorithm<ref name="Pham & Castellani, 2009"/><ref name="Pham & Ghanbarzadeh et. a. 2006">Pham D.T., Ghanbarzadeh A., Koc E., Otri S., Rahim S., Zaidi M., [~~http~~://www.academia.edu/~~download~~/~~37269572/Pham06_-_The_Bee_Algorithm.pdf~~ The Bees Algorithm, A Novel Tool for Complex Optimisation Problems]{{dead link\|date=July 2022\|bot=medic}}{{cbignore\|bot=medic}}, Proc 2nd Int Virtual Conf on Intelligent Production Machines and Systems (IPROMS 2006), Oxford: Elsevier, pp. 454-459, 2006.</ref> mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Each candidate solution is thought of as a food source (flower), and a population (colony) of ''n'' agents (bees) is used to search the solution space. Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness).		The bees algorithm<ref name="Pham & Castellani, 2009"/><ref name="Pham & Ghanbarzadeh et. a. 2006">Pham D.T., Ghanbarzadeh A., Koc E., Otri S., Rahim S., Zaidi M., [https://www.academia.edu/8358231/The_Bees_Algorithm_A_Novel_Tool_for_Complex_Optimisation_Problems The Bees Algorithm, A Novel Tool for Complex Optimisation Problems]{{dead link\|date=July 2022\|bot=medic}}{{cbignore\|bot=medic}}, Proc 2nd Int Virtual Conf on Intelligent Production Machines and Systems (IPROMS 2006), Oxford: Elsevier, pp. 454-459, 2006.</ref> mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Each candidate solution is thought of as a food source (flower), and a population (colony) of ''n'' agents (bees) is used to search the solution space. Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness).

	The bees algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number ''T'' of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures: recruitment, local search, neighbourhood shrinking, site abandonment, and global search.		The bees algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number ''T'' of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures: recruitment, local search, neighbourhood shrinking, site abandonment, and global search.

InternetArchiveBot: Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.5

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← Previous revision		Revision as of 10:16, 2 October 2023
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	== Metaphor ==		== Metaphor ==
	A colony of [[honey bees]] can extend itself over long distances (over 14 km)<ref name="Tereshko & Loengarov, 2005">Tereshko V., Loengarov A., (2005) [http://cis.uws.ac.uk/research/journal/V9/V9N3/bees.pdf Collective Decision-Making in Honey Bee Foraging Dynamics]. Journal of Computing and Information Systems, 9(3), 1-7.</ref> and in multiple directions simultaneously to harvest nectar or pollen from multiple food sources (flower patches).		A colony of [[honey bees]] can extend itself over long distances (over 14 km)<ref name="Tereshko & Loengarov, 2005">Tereshko V., Loengarov A., (2005) [http://cis.uws.ac.uk/research/journal/V9/V9N3/bees.pdf Collective Decision-Making in Honey Bee Foraging Dynamics] {{Webarchive\|url=https://web.archive.org/web/20140201175130/http://cis.uws.ac.uk/research/journal/V9/V9N3/bees.pdf \|date=2014-02-01 }}. Journal of Computing and Information Systems, 9(3), 1-7.</ref> and in multiple directions simultaneously to harvest nectar or pollen from multiple food sources (flower patches).
	A small fraction of the colony constantly searches the environment looking for new flower patches. These scout bees move randomly in the area surrounding the hive, evaluating the profitability (net energy yield) of the food sources encountered.<ref name="Tereshko & Loengarov, 2005"/> When they return to the hive, the scouts deposit the food harvested. Those individuals that found a highly profitable food source go to an area in the hive called the “dance floor”, and perform a ritual known as the [[waggle dance]].<ref>Von Frisch, K. (1967) The Dance Language and Orientation of Bees. Harvard University Press, Cambridge, Massachusetts.</ref>		A small fraction of the colony constantly searches the environment looking for new flower patches. These scout bees move randomly in the area surrounding the hive, evaluating the profitability (net energy yield) of the food sources encountered.<ref name="Tereshko & Loengarov, 2005"/> When they return to the hive, the scouts deposit the food harvested. Those individuals that found a highly profitable food source go to an area in the hive called the “dance floor”, and perform a ritual known as the [[waggle dance]].<ref>Von Frisch, K. (1967) The Dance Language and Orientation of Bees. Harvard University Press, Cambridge, Massachusetts.</ref>
	Through the waggle dance a scout bee communicates the location of its discovery to idle onlookers, which join in the exploitation of the flower patch. Since the length of the dance is proportional to the scout’s rating of the food source, more foragers get recruited to harvest the best rated flower patches. After dancing, the scout returns to the food source it discovered to collect more food.		Through the waggle dance a scout bee communicates the location of its discovery to idle onlookers, which join in the exploitation of the flower patch. Since the length of the dance is proportional to the scout’s rating of the food source, more foragers get recruited to harvest the best rated flower patches. After dancing, the scout returns to the food source it discovered to collect more food.

GreenC bot: Rescued 1 archive link; reformat 1 link. Wayback Medic 2.5

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Rescued 1 archive link; reformat 1 link. Wayback Medic 2.5

← Previous revision		Revision as of 05:29, 18 July 2022
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	== Algorithm ==		== Algorithm ==
	The bees algorithm<ref name="Pham & Castellani, 2009"/><ref name="Pham & Ghanbarzadeh et. a. 2006">Pham D.T., Ghanbarzadeh A., Koc E., Otri S., Rahim S., Zaidi M., [http://www.academia.edu/download/37269572/Pham06_-_The_Bee_Algorithm.pdf The Bees Algorithm, A Novel Tool for Complex Optimisation Problems], Proc 2nd Int Virtual Conf on Intelligent Production Machines and Systems (IPROMS 2006), Oxford: Elsevier, pp. 454-459, 2006.</ref> mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Each candidate solution is thought of as a food source (flower), and a population (colony) of ''n'' agents (bees) is used to search the solution space. Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness).		The bees algorithm<ref name="Pham & Castellani, 2009"/><ref name="Pham & Ghanbarzadeh et. a. 2006">Pham D.T., Ghanbarzadeh A., Koc E., Otri S., Rahim S., Zaidi M., [http://www.academia.edu/download/37269572/Pham06_-_The_Bee_Algorithm.pdf The Bees Algorithm, A Novel Tool for Complex Optimisation Problems]{{dead link\|date=July 2022\|bot=medic}}{{cbignore\|bot=medic}}, Proc 2nd Int Virtual Conf on Intelligent Production Machines and Systems (IPROMS 2006), Oxford: Elsevier, pp. 454-459, 2006.</ref> mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Each candidate solution is thought of as a food source (flower), and a population (colony) of ''n'' agents (bees) is used to search the solution space. Each time an artificial bee visits a flower (lands on a solution), it evaluates its profitability (fitness).

	The bees algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number ''T'' of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures: recruitment, local search, neighbourhood shrinking, site abandonment, and global search.		The bees algorithm consists of an initialisation procedure and a main search cycle which is iterated for a given number ''T'' of times, or until a solution of acceptable fitness is found. Each search cycle is composed of five procedures: recruitment, local search, neighbourhood shrinking, site abandonment, and global search.

Vedantzope: added References and workshop link

2022-05-19T18:53:48Z

added References and workshop link

← Previous revision		Revision as of 18:53, 19 May 2022
Line 1:		Line 1:
	{{distinguish\|Artificial bee colony algorithm}}		{{distinguish\|Artificial bee colony algorithm}}
	In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref>		In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>

	== Metaphor ==		== Metaphor ==
Line 132:		Line 132:
	*[http://beesalgorithmsite.altervista.org/ The bees algorithm website]		*[http://beesalgorithmsite.altervista.org/ The bees algorithm website]
	*[http://news.bbc.co.uk/1/hi/wales/south_east/5285106.stm Boffins put dancing bees to work – BBC News]		*[http://news.bbc.co.uk/1/hi/wales/south_east/5285106.stm Boffins put dancing bees to work – BBC News]
			*[https://sites.google.com/view/baaworkshop/home The bees algorithm workshop]

	{{collective animal behaviour}}		{{collective animal behaviour}}

MrOllie: Restored revision 1051526074 by MrOllie (talk): Clearly false. This article has been perfectly understandable for the 13 years it has existed before you added this cite, which adds no new information.

2021-10-24T12:26:03Z

Restored revision 1051526074 by MrOllie (talk): Clearly false. This article has been perfectly understandable for the 13 years it has existed before you added this cite, which adds no new information.

← Previous revision		Revision as of 12:26, 24 October 2021
Line 1:		Line 1:
	{{distinguish\|Artificial bee colony algorithm}}		{{distinguish\|Artificial bee colony algorithm}}
	In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref name="Baronti & Castellani & Pham 2020">Baronti, L., Castellani, M., Pham, D.T., (2020), [https://www.sciencedirect.com/science/article/pii/S2210650220303990 An Analysis of the Search Mechanisms of the Bees Algorithm], Swarm and Evolutionary Computation, 59:100746; ({{doi\| 10.1016/j.swevo.2020.100746}})</ref>		In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref>

	== Metaphor ==		== Metaphor ==

← Previous revision		Revision as of 17:13, 11 April 2025
Line 1:		Line 1:
	{{Short description\|Population-based search algorithm}}		{{Short description\|Population-based search algorithm}}
	{{distinguish\|Artificial bee colony algorithm}}		{{distinguish\|Artificial bee colony algorithm}}
			[[File:Honey_Bee_takes_Nectar.JPG \| thumb \| right \| The bee algorithm mimics the foraging behavior of honey bees.]]
	In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>		In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>

← Previous revision		Revision as of 16:18, 25 April 2024
Line 1:		Line 1:
			{{Short description\|Population-based search algorithm}}
	{{distinguish\|Artificial bee colony algorithm}}		{{distinguish\|Artificial bee colony algorithm}}
	In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>		In [[computer science]] and [[operations research]], the '''bees algorithm''' is a population-based [[search algorithm]] which was developed by Pham, Ghanbarzadeh et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [http://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems]. Proc. ImechE, Part C, 223(12), 2919-2938.</ref><ref>Pham, D.T. and Castellani, M. (2013), [https://link.springer.com/article/10.1007/s00500-013-1104-9 Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms], Soft Computing, 1-33.</ref><ref>Pham, D.T. and Castellani, M. (2015), [http://www.tandfonline.com/doi/abs/10.1080/23311916.2015.1091540 A comparative study of the bees algorithm as a tool for function optimisation], Cogent Engineering 2(1), 1091540.</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), [http://www.mdpi.com/2073-431X/6/1/5 Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm], Computers 2017, 6(1), 5; ({{doi\|10.3390/computers6010005}})</ref><ref>Baronti, Luca & Castellani, Marco & Pham, D.. (2020),[http://pure-oai.bham.ac.uk/ws/files/100752622/An_Analysis_of_the_Search_Mechanisms_of_the_Bees_Algorithm.pdf. An Analysis of the Search Mechanisms of the Bees Algorithm.], Swarm and Evolutionary Computation. 59. 100746. 10.1016/j.swevo.2020.100746</ref>