Brain storm optimization algorithm - Revision history

Citation bot: Added bibcode. | Use this bot. Report bugs. | Suggested by Dominic3203 | Category:Algorithms and data structures stubs | #UCB_Category 53/92

2024-10-18T16:42:00Z

Added bibcode. | Use this bot. Report bugs. | Suggested by Dominic3203 | Category:Algorithms and data structures stubs | #UCB_Category 53/92

← Previous revision		Revision as of 16:42, 18 October 2024
Line 1:		Line 1:
	{{context\|date=November 2019}}		{{context\|date=November 2019}}

	The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 \|s2cid=120591309 }}</ref>		The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 \|bibcode=2014NonDy..78.1973Q \|s2cid=120591309 }}</ref>

	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>
Line 7:		Line 7:
	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{citation needed\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{citation needed\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free \|bibcode=2018IEEEA...619968S }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 \|s2cid=199379609 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 \|s2cid=199379609 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

Jarble: adding Template:Optimization algorithms

2023-03-26T23:35:44Z

adding Template:Optimization algorithms

← Previous revision		Revision as of 23:35, 26 March 2023
Line 13:		Line 13:
	==References==		==References==
	{{reflist}}		{{reflist}}

			{{Optimization algorithms}}

	[[Category:Heuristic algorithms]]		[[Category:Heuristic algorithms]]

Citation bot: Add: s2cid. | Use this bot. Report bugs. | Suggested by Abductive | Category:Algorithms and data structures stubs | #UCB_Category 17/84

2021-11-15T05:19:21Z

Add: s2cid. | Use this bot. Report bugs. | Suggested by Abductive | Category:Algorithms and data structures stubs | #UCB_Category 17/84

← Previous revision		Revision as of 05:19, 15 November 2021
Line 9:		Line 9:
	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 \|s2cid=199379609 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

	==References==		==References==

Citation bot: Alter: template type. Add: issue, year, s2cid. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 19/162

2021-06-27T20:16:42Z

Alter: template type. Add: issue, year, s2cid. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 19/162

← Previous revision		Revision as of 20:16, 27 June 2021
Line 1:		Line 1:
	{{context\|date=November 2019}}		{{context\|date=November 2019}}

	The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 }}</ref>		The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 \|s2cid=120591309 }}</ref>

	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>
Line 9:		Line 9:
	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite ~~journal~~ \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

	==References==		==References==

Headbomb: clean up, replaced: |journal=Springer → |publisher=Springer

2021-06-27T19:20:59Z

clean up, replaced: |journal=Springer → |publisher=Springer

← Previous revision		Revision as of 19:20, 27 June 2021
Line 5:		Line 5:
	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>

	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{citation needed\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|~~journal~~=Springer Nature ~~Switzerland AG, Cham~~ \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

	==References==		==References==
	{{reflist}}		{{reflist}}

	[[Category:Heuristic algorithms]]		[[Category:Heuristic algorithms]]


	{{Algorithm-stub}}		{{Algorithm-stub}}

AnomieBOT: Dating maintenance tags: {{Cn}}

2020-09-04T22:08:53Z

Dating maintenance tags: {{Cn}}

← Previous revision		Revision as of 22:08, 4 September 2020
Line 5:		Line 5:
	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>

	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

Headbomb: free doi

2020-09-04T21:48:45Z

free doi

← Previous revision		Revision as of 21:48, 4 September 2020
Line 7:		Line 7:
	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 ~~\|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017~~\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

Headbomb: ce

2020-09-04T21:48:22Z

← Previous revision		Revision as of 21:48, 4 September 2020
Line 9:		Line 9:
	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=~~International Federation of Automatic Control~~ \|volume=51 \|pages=13–18 \|~~url~~=~~https:~~/~~/www~~.~~sciencedirect~~.~~com/science/article/pii/S2405896318333883~~ \|~~accessdate~~=~~6 November 2018~~}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

	==References==		==References==

Headbomb: ce

2020-09-04T21:47:45Z

← Previous revision		Revision as of 21:47, 4 September 2020
Line 9:		Line 9:
	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=International Federation of Automatic Control \|volume=51 \|pages=13–18 \|url=https://www.sciencedirect.com/science/article/pii/S2405896318333883 \|accessdate=6 November 2018}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well. <ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun ~~Steed~~ \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal= ~~ENGINEERING~~ and ~~TECHNOLOGY~~ ~~RESEARCH~~ \|~~url~~=~~http://dpi-proceedings~~.~~com/index.php~~/dtetr/~~article/view~~/20342 \|~~accessdate~~=~~16 December 2017~~}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=International Federation of Automatic Control \|volume=51 \|pages=13–18 \|url=https://www.sciencedirect.com/science/article/pii/S2405896318333883 \|accessdate=6 November 2018}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

	==References==		==References==

Headbomb: -predatory source

2020-09-04T21:46:24Z

-predatory source

← Previous revision		Revision as of 21:46, 4 September 2020
Line 5:		Line 5:
	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>

	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,~~<ref>~~{{cite journal \|last1=Qi \|first1=Yaqian \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun Steed \|title=Analysis of Risk Management for the Coal Mine Operations \|journal=Energy and Power Engineering \|volume=10 \|pages=1–7 \|url=https://www.researchgate.net/publication/309666155 \|accessdate=6 April 2017}}~~</ref>~~ and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

← Previous revision		Revision as of 16:42, 18 October 2024
Line 1:		Line 1:
	{{context\|date=November 2019}}		{{context\|date=November 2019}}

	The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 \|s2cid=120591309 }}</ref>		The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 \|bibcode=2014NonDy..78.1973Q \|s2cid=120591309 }}</ref>

	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>
Line 7:		Line 7:
	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{citation needed\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{citation needed\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free \|bibcode=2018IEEEA...619968S }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 \|s2cid=199379609 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 \|s2cid=199379609 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

← Previous revision		Revision as of 20:16, 27 June 2021
Line 1:		Line 1:
	{{context\|date=November 2019}}		{{context\|date=November 2019}}

	The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 }}</ref>		The '''brain storm optimization''' algorithm is a [[heuristic algorithm]] that focuses on solving multi-modal problems, such as [[radio antennas]] design worked on by [[Yahya Rahmat-Samii]], inspired by the [[brainstorming]] process, proposed by Dr. [[Yuhui Shi]].<ref>{{cite book \|last=Shi \|first=Yuhui \|year=2011 \|chapter=Brain Storm Optimization Algorithm \|editor-last1=Tan \|editor-first1=Y. \|editor-last2=Shi \|editor-first2=Y. \|editor-last3=Chai \|editor-first3=Y. \|editor-last4=Wang \|editor-first4=G. \|title=Advances in Swarm Intelligence \|volume=6728 \|pages=303–309 \|doi=10.1007/978-3-642-21515-5_36\|isbn=978-3-642-21514-8 \|series=Lecture Notes in Computer Science }}</ref><ref>{{cite journal \|last1=Qiu \|first1=Huaxin \|last2=Duan \|first2=Haibin \|title=Receding horizon control for multiple UAV formation flight based on modified brain storm optimization \|journal=Nonlinear Dynamics \|volume=78 \|issue=3 \|pages=1973–1988 \|doi=10.1007/s11071-014-1579-7\|year=2014 \|s2cid=120591309 }}</ref>

	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>
Line 9:		Line 9:
	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite ~~journal~~ \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|year=2018 \|volume=51 \|issue=28 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite book \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|publisher=Springer Nature \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

	==References==		==References==

← Previous revision		Revision as of 22:08, 4 September 2020
Line 5:		Line 5:
	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>

	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn\|date=September 2020}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

← Previous revision		Revision as of 21:48, 4 September 2020
Line 7:		Line 7:
	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 ~~\|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017~~\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>

	A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>		A number of comparison studies are conducted between [[Particle swarm optimization\|PSO]] and BSO.<ref>{{cite journal \|last1=Sato \|first1=Mayuko \|last2=Fukuyama \|first2=Yoshikazu \|title=Total Optimization of Smart City by Modified Brain Storm Optimization \|journal=IFAC-PapersOnLine \|volume=51 \|pages=13–18 \|doi=10.1016/j.ifacol.2018.11.670 \|doi-access=free}}</ref> Recently published book contains much more up to date references.<ref>{{cite journal \|last1=Cheng \|first1=S. \|last2=Shi \|first2=Y. \|title=Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books\|journal=Springer Nature Switzerland AG, Cham \|volume=23 \|doi=10.1007/978-3-030-15070-9 \|isbn=978-3-030-15069-3 \|series=Adaptation, Learning, and Optimization \|year=2019 }}</ref> It was used to design 5G network as well.<ref>{{cite journal \|last1=Wu \|first1=Qiong \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun S. \|title=A Quantum Twin Brain Storm Optimization for Fog Computing in 5G \|journal=DEStech Transactions on Engineering and Technology Research \|date=3 April 2018 \|issue=icmm \|doi=10.12783/dtetr/icmm2017/20342 \|doi-access=free}}</ref>

← Previous revision		Revision as of 21:46, 4 September 2020
Line 5:		Line 5:
	More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>		More than 200 papers related to BSO algorithms have appeared in various journals and conferences. There have also been special issues and special sessions on Brain Storm Optimization algorithm in journals and various conferences, such as Memetic Computing Journal.<ref>{{cite web \|title=Keynote Speakers-ICCEM 2019 \|url=https://iccem2019.tongji.edu.cn/Keynote_Speakers.htm \|publisher=ICCEM 2019 conference \|accessdate=16 August 2019}}</ref><ref>{{cite journal \|last1=Cheng \|first1=Shi \|last2=Shi \|first2=Yuhui \|title=Thematic issue on "Brain Storm Optimization Algorithms" \|journal=Memetic Computing \|volume=10 \|issue=4 \|pages=351–352 \|doi=10.1007/s12293-018-0276-3\|year=2018 \|doi-access=free }}</ref>

	There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,~~<ref>~~{{cite journal \|last1=Qi \|first1=Yaqian \|last2=Xu \|first2=Tong \|last3=Huang \|first3=Jun Steed \|title=Analysis of Risk Management for the Coal Mine Operations \|journal=Energy and Power Engineering \|volume=10 \|pages=1–7 \|url=https://www.researchgate.net/publication/309666155 \|accessdate=6 April 2017}}~~</ref>~~ and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>		There are a number of variants of the algorithms as well, such as Hypo Variance Brain Storm Optimization, where the object function evaluation is based on the hypo or sub variance rather than Gaussian variance,{{cn}} and Global-best Brain Storm Optimization, where the global-best incorporates a re-initialization scheme that is triggered by the current state of the population, combined with per-variable updates and fitness-based grouping.<ref>{{cite journal \|last1=El-Abd \|first1=Mohammed \|title=Global-best brain storm optimization algorithm \|journal=Swarm and Evolutionary Computation \|volume=37 \|pages=27–44 \|doi=10.1016/j.swevo.2017.05.001 \|year=2017 }}</ref>

	[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>		[[Carleton University]] researchers proposed another variant by using a periodic [[quantum]] learning strategy to provides new momentum, enabling individuals to escape local optima ([[local optimum]]).<ref>{{cite journal \|last1=Song \|first1=Zhenshou \|last2=Peng \|first2=Jiaqi \|last3=Li \|first3=Chunquan\|last4=Liu \|first4=Peter X. \|title=A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy \|journal=IEEE Access \|volume=6 \|pages=19968–19983 \|url=https://ir.library.carleton.ca/pub/16853 \|accessdate=23 November 2017\|doi=10.1109/ACCESS.2017.2776958 \|year=2018 \|doi-access=free }}</ref>