Vote for your favorite mineral in #MinCup25! - Baryte vs. Hematite
It's a heavyweight match between industrial powerhouses as soft #baryte competes against rusty red #hematite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Li, Xinxin, Hua, Shuai, Liu, Qunfeng, Li, Yun (2023) A partition-based convergence framework for population-based optimization algorithms. Information Sciences, 627. 169-188 doi:10.1016/j.ins.2023.01.085

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleA partition-based convergence framework for population-based optimization algorithms
JournalInformation Sciences
AuthorsLi, XinxinAuthor
Hua, ShuaiAuthor
Liu, QunfengAuthor
Li, YunAuthor
Year2023 (May)Volume627
PublisherElsevier BV
DOIdoi:10.1016/j.ins.2023.01.085Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID15690718Long-form Identifiermindat:1:5:15690718:9
GUID0
Full ReferenceLi, Xinxin, Hua, Shuai, Liu, Qunfeng, Li, Yun (2023) A partition-based convergence framework for population-based optimization algorithms. Information Sciences, 627. 169-188 doi:10.1016/j.ins.2023.01.085
Plain TextLi, Xinxin, Hua, Shuai, Liu, Qunfeng, Li, Yun (2023) A partition-based convergence framework for population-based optimization algorithms. Information Sciences, 627. 169-188 doi:10.1016/j.ins.2023.01.085
In(2023) Information Sciences Vol. 627. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Liu, Qunfeng, Jing, Yunpeng, Yan, Yuan, Li, Yun (2024) Mean-based Borda count for paradox-free comparisons of optimization algorithms. Information Sciences, 660. 120120 doi:10.1016/j.ins.2024.120120
Liu, Qunfeng, Wei, Wenhong, Yuan, Huaqiang, Zhan, Zhi-Hui, Li, Yun (2016) Topology selection for particle swarm optimization. Information Sciences, 363. 154-173 doi:10.1016/j.ins.2016.04.050
Yildiz, Ali R. (2012) A comparative study of population-based optimization algorithms for turning operations. Information Sciences, 210. 81-88 doi:10.1016/j.ins.2012.03.005
Li, Chunlei, Sun, Gaoji, Deng, Libao, Qiao, Liyan, Yang, Guoqing (2023) A population state evaluation-based improvement framework for differential evolution. Information Sciences, 629. 15-38 doi:10.1016/j.ins.2023.01.120
Xu, Hua-Qiang, Gu, Shuai, Fan, Yu-Cheng, Li, Xiao-Shuang, Zhao, Yue-Feng, Zhao, Jun, Wang, Jing-Jing (2023) A strategy learning framework for particle swarm optimization algorithm. Information Sciences, 619. 126-152 doi:10.1016/j.ins.2022.10.069
Ai, Wu, Chen, Weisheng, Xie, Jin (2017) A general framework for population-based distributed optimization over networks. Information Sciences, 418. 136-152 doi:10.1016/j.ins.2017.08.010
Oszust, Mariusz, Sroka, Grzegorz, Cymerys, Karol (2021) A hybridization approach with predicted solution candidates for improving population-based optimization algorithms. Information Sciences, 574. 133-161 doi:10.1016/j.ins.2021.04.082
Li, Hua, Cong, Runmin, Kwong, Sam, Chen, Chuanbo, Xu, Qianqian, Li, Chongyi (2021) Stereo superpixel: An iterative framework based on parallax consistency and collaborative optimization. Information Sciences, 556. 209-222 doi:10.1016/j.ins.2020.12.031
Qin, Yifan, Deng, Libao, Li, Chunlei, Zhang, Lili (2024) A dimensional difference-based population size adjustment framework for differential evolution. Information Sciences, 660. 120110 doi:10.1016/j.ins.2024.120110
Wang, Zhi-Jun, Gao, He-Bei, Wang, Xiang-Hong, Zhao, Shuai-Ye, Li, Hong, Zhang, Xiao-Qin (2023) Adaptive learning rate optimization algorithms with dynamic bound based on Barzilai-Borwein method. Information Sciences, 634. 42-54 doi:10.1016/j.ins.2023.03.050
Wang, Bing-Chuan, Li, Han-Xiong, Feng, Yun (2018) An improved teaching-learning-based optimization for constrained evolutionary optimization. Information Sciences, 456. 131-144 doi:10.1016/j.ins.2018.04.083
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 19, 2025 05:25:44
Go to top of page