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{{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>
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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>

Latest revision as of 16:42, 18 October 2024

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.[1][2]

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.[3][4]

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] 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.[5]

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).[6]

A number of comparison studies are conducted between PSO and BSO.[7] Recently published book contains much more up to date references.[8] It was used to design 5G network as well.[9]

References

[edit]
  1. ^ Shi, Yuhui (2011). "Brain Storm Optimization Algorithm". In Tan, Y.; Shi, Y.; Chai, Y.; Wang, G. (eds.). Advances in Swarm Intelligence. Lecture Notes in Computer Science. Vol. 6728. pp. 303–309. doi:10.1007/978-3-642-21515-5_36. ISBN 978-3-642-21514-8.
  2. ^ Qiu, Huaxin; Duan, Haibin (2014). "Receding horizon control for multiple UAV formation flight based on modified brain storm optimization". Nonlinear Dynamics. 78 (3): 1973–1988. Bibcode:2014NonDy..78.1973Q. doi:10.1007/s11071-014-1579-7. S2CID 120591309.
  3. ^ "Keynote Speakers-ICCEM 2019". ICCEM 2019 conference. Retrieved 16 August 2019.
  4. ^ Cheng, Shi; Shi, Yuhui (2018). "Thematic issue on "Brain Storm Optimization Algorithms"". Memetic Computing. 10 (4): 351–352. doi:10.1007/s12293-018-0276-3.
  5. ^ El-Abd, Mohammed (2017). "Global-best brain storm optimization algorithm". Swarm and Evolutionary Computation. 37: 27–44. doi:10.1016/j.swevo.2017.05.001.
  6. ^ Song, Zhenshou; Peng, Jiaqi; Li, Chunquan; Liu, Peter X. (2018). "A Simple Brain Storm Optimization Algorithm With a Periodic Quantum Learning Strategy". IEEE Access. 6: 19968–19983. Bibcode:2018IEEEA...619968S. doi:10.1109/ACCESS.2017.2776958.
  7. ^ Sato, Mayuko; Fukuyama, Yoshikazu (2018). "Total Optimization of Smart City by Modified Brain Storm Optimization". IFAC-PapersOnLine. 51 (28): 13–18. doi:10.1016/j.ifacol.2018.11.670.
  8. ^ Cheng, S.; Shi, Y. (2019). Brain Storm Optimization Algorithms: Concepts, Principles and Applications, Part of Adaptation, Learning and Optimization Books. Adaptation, Learning, and Optimization. Vol. 23. Springer Nature. doi:10.1007/978-3-030-15070-9. ISBN 978-3-030-15069-3. S2CID 199379609.
  9. ^ Wu, Qiong; Xu, Tong; Huang, Jun S. (3 April 2018). "A Quantum Twin Brain Storm Optimization for Fog Computing in 5G". DEStech Transactions on Engineering and Technology Research (icmm). doi:10.12783/dtetr/icmm2017/20342.


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