A Collaborative Neurodynamic Approach to Distributed Global Optimization
In this article, we present a collaborative neurodynamic approach to distributed optimization with nonconvex functions. We develop a recurrent neural network (RNN) group by connecting individual projection neural networks through a communication network. We prove the convergence of the RNN group to...
Saved in:
| Published in | IEEE transactions on systems, man, and cybernetics. Systems Vol. 53; no. 5; pp. 3141 - 3151 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In this article, we present a collaborative neurodynamic approach to distributed optimization with nonconvex functions. We develop a recurrent neural network (RNN) group by connecting individual projection neural networks through a communication network. We prove the convergence of the RNN group to the local optimal solutions of a given distributed optimization problem. We propose a collaborative neurodynamic optimization system with multiple RNN groups for scattered searches and a metaheuristic rule for reinitializing the neuronal states upon their local convergence. We elaborate on three numerical examples to demonstrate the efficacy of the proposed approach to distributed global optimization in the presence of nonconvexity. |
|---|---|
| AbstractList | In this article, we present a collaborative neurodynamic approach to distributed optimization with nonconvex functions. We develop a recurrent neural network (RNN) group by connecting individual projection neural networks through a communication network. We prove the convergence of the RNN group to the local optimal solutions of a given distributed optimization problem. We propose a collaborative neurodynamic optimization system with multiple RNN groups for scattered searches and a metaheuristic rule for reinitializing the neuronal states upon their local convergence. We elaborate on three numerical examples to demonstrate the efficacy of the proposed approach to distributed global optimization in the presence of nonconvexity. |
| Author | Xia, Zicong Wang, Jun Liu, Yang |
| Author_xml | – sequence: 1 givenname: Zicong orcidid: 0000-0001-9943-5087 surname: Xia fullname: Xia, Zicong email: 201531700128@zjnu.edu.cn organization: College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China – sequence: 2 givenname: Yang orcidid: 0000-0003-3761-0104 surname: Liu fullname: Liu, Yang email: liuyang@zjnu.edu.cn organization: Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province and College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China – sequence: 3 givenname: Jun orcidid: 0000-0002-1305-5735 surname: Wang fullname: Wang, Jun email: jwang.cs@cityu.edu.hk organization: Department of Computer Science and the School of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong |
| BookMark | eNp9kD9PwzAQxS1UJErpB0AskZhTznbqP2MVoEUqdKDMluM6wlUaB8dBKp-elKIODEx3Or3f3bt3iQa1ry1C1xgmGIO8W78-5xMChEwoIVhSfoaGBDOREkLJ4NRjdoHGbbsFAEwEo8CGaDFLcl9VuvBBR_dpkxfbBb_Z13rnTDJrmuC1eU-iT-5dG4Mrumg3ybzyha6SVRPdzn31oK-v0Hmpq9aOf-sIvT0-rPNFulzNn_LZMjVE0piW2Jis5CLTwAkGmoEUOhMbDmCKfpwB5YBBaE0yrjGjVrD-Cyy0BUynlI7Q7XFv7-yjs21UW9-Fuj-piACQjEjOexU_qkzwbRtsqYyLPz5j0K5SGNQhOXVITh2SU7_J9ST-QzbB7XTY_8vcHBlnrT3ppWSUTQX9BqhHeRc |
| CODEN | ITSMFE |
| CitedBy_id | crossref_primary_10_1109_TCYB_2024_3394394 crossref_primary_10_1109_TNSE_2024_3401748 crossref_primary_10_1109_TSMC_2023_3336200 crossref_primary_10_1007_s11432_022_3781_4 crossref_primary_10_1109_TETCI_2024_3369667 crossref_primary_10_1109_TSMC_2024_3417900 crossref_primary_10_1016_j_neunet_2023_06_003 crossref_primary_10_1016_j_cnsns_2024_107999 crossref_primary_10_1109_TSMC_2023_3331260 crossref_primary_10_1016_j_neunet_2024_106525 crossref_primary_10_1016_j_neunet_2023_10_011 crossref_primary_10_1016_j_neunet_2023_10_022 crossref_primary_10_1016_j_neunet_2025_107257 crossref_primary_10_1016_j_neunet_2024_106791 crossref_primary_10_1109_JAS_2024_124935 crossref_primary_10_1016_j_neunet_2023_12_011 crossref_primary_10_1016_j_neunet_2024_107027 |
| Cites_doi | 10.1109/TSMC.2016.2531649 10.1109/TNNLS.2016.2582381 10.2307/j.ctvcm4hcj 10.1109/TCNS.2020.3024321 10.1109/TAC.2018.2862471 10.1109/TNNLS.2021.3098668 10.1137/140975991 10.1109/TCNS.2020.3027787 10.1109/ALLERTON.2010.5706956 10.1109/TAC.2015.2416927 10.1109/TNNLS.2019.2918984 10.1109/TCYB.2020.3031687 10.1109/TSMC.2021.3138109 10.1109/TNNLS.2021.3139865 10.1109/TNNLS.2014.2387862 10.1109/TCYB.2017.2759141 10.1109/TAC.2013.2278132 10.1109/TNNLS.2021.3068500 10.1109/4235.985692 10.1109/TAC.2020.2979274 10.1109/TSMC.2019.2957156 10.1109/TETCI.2017.2716377 10.1109/TSMC.2014.2332306 10.1016/j.ins.2018.01.040 10.1016/j.arcontrol.2019.05.006 10.1137/030602770 10.1109/TNSE.2021.3133541 10.1109/TAC.2021.3071651 10.1137/1.9780898719451 10.1162/089976604322860730 10.1109/TNNLS.2016.2549566 10.1109/TNNLS.2021.3082528 10.1109/TAC.2017.2648041 10.1109/TSMC.2019.2898551 10.1109/81.995659 10.1109/TAC.2008.2009515 10.1109/TAC.2020.3034869 10.1016/j.neunet.2019.02.002 10.1109/TSMC.2019.2933005 10.1016/j.automatica.2021.109899 10.1109/TCYB.2017.2766762 10.1109/TNNLS.2016.2524619 10.1109/TCYB.2019.2948424 10.1109/TCSI.2004.830694 10.1109/TCYB.2021.3102941 10.1016/j.automatica.2018.05.013 10.1109/TCYB.2017.2760908 10.1109/TSMC.2019.2931846 10.1109/TAC.2015.2449811 10.1007/978-3-540-72395-0_25 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 7TB 8FD FR3 H8D JQ2 L7M L~C L~D |
| DOI | 10.1109/TSMC.2022.3221937 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Aerospace Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Aerospace Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Aerospace Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2168-2232 |
| EndPage | 3151 |
| ExternalDocumentID | 10_1109_TSMC_2022_3221937 9963658 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Research Grants Council of the Hong Kong Special Administrative Region of China through the General Research Fund grantid: 11202318; 11202019; 11203721 funderid: 10.13039/501100002920 – fundername: Jinhua Science and Technology Project grantid: 2022-1-042 – fundername: Natural Science Foundation of Zhejiang Province of China grantid: LR20F030001; LD19A010001 funderid: 10.13039/501100004731 – fundername: National Natural Science Foundation of China grantid: 62173308 funderid: 10.13039/501100001809 |
| GroupedDBID | 0R~ 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFS ACIWK AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION RIG 7SC 7SP 7TB 8FD FR3 H8D JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c293t-f1cc4f784a0721034098a48d700cb78440370108aa247a163e8622318ae013533 |
| IEDL.DBID | RIE |
| ISSN | 2168-2216 |
| IngestDate | Mon Jun 30 04:28:29 EDT 2025 Tue Jul 01 02:29:22 EDT 2025 Thu Apr 24 23:06:47 EDT 2025 Wed Aug 27 02:55:20 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c293t-f1cc4f784a0721034098a48d700cb78440370108aa247a163e8622318ae013533 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-3761-0104 0000-0002-1305-5735 0000-0001-9943-5087 |
| PQID | 2800962977 |
| PQPubID | 75739 |
| PageCount | 11 |
| ParticipantIDs | proquest_journals_2800962977 ieee_primary_9963658 crossref_citationtrail_10_1109_TSMC_2022_3221937 crossref_primary_10_1109_TSMC_2022_3221937 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-05-01 |
| PublicationDateYYYYMMDD | 2023-05-01 |
| PublicationDate_xml | – month: 05 year: 2023 text: 2023-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on systems, man, and cybernetics. Systems |
| PublicationTitleAbbrev | TSMC |
| PublicationYear | 2023 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref15 ref14 ref11 ref10 ref17 ref16 ref19 ref18 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 |
| References_xml | – ident: ref19 doi: 10.1109/TSMC.2016.2531649 – ident: ref36 doi: 10.1109/TNNLS.2016.2582381 – ident: ref47 doi: 10.2307/j.ctvcm4hcj – ident: ref32 doi: 10.1109/TCNS.2020.3024321 – ident: ref15 doi: 10.1109/TAC.2018.2862471 – ident: ref17 doi: 10.1109/TNNLS.2021.3098668 – ident: ref29 doi: 10.1137/140975991 – ident: ref31 doi: 10.1109/TCNS.2020.3027787 – ident: ref44 doi: 10.1109/ALLERTON.2010.5706956 – ident: ref11 doi: 10.1109/TAC.2015.2416927 – ident: ref40 doi: 10.1109/TNNLS.2019.2918984 – ident: ref27 doi: 10.1109/TCYB.2020.3031687 – ident: ref26 doi: 10.1109/TSMC.2021.3138109 – ident: ref28 doi: 10.1109/TNNLS.2021.3139865 – ident: ref35 doi: 10.1109/TNNLS.2014.2387862 – ident: ref7 doi: 10.1109/TCYB.2017.2759141 – ident: ref10 doi: 10.1109/TAC.2013.2278132 – ident: ref42 doi: 10.1109/TNNLS.2021.3068500 – ident: ref43 doi: 10.1109/4235.985692 – ident: ref8 doi: 10.1109/TAC.2020.2979274 – ident: ref16 doi: 10.1109/TSMC.2019.2957156 – ident: ref13 doi: 10.1109/TETCI.2017.2716377 – ident: ref18 doi: 10.1109/TSMC.2014.2332306 – ident: ref20 doi: 10.1016/j.ins.2018.01.040 – ident: ref1 doi: 10.1016/j.arcontrol.2019.05.006 – ident: ref50 doi: 10.1137/030602770 – ident: ref25 doi: 10.1109/TNSE.2021.3133541 – ident: ref22 doi: 10.1109/TAC.2021.3071651 – ident: ref45 doi: 10.1137/1.9780898719451 – ident: ref49 doi: 10.1162/089976604322860730 – ident: ref12 doi: 10.1109/TNNLS.2016.2549566 – ident: ref38 doi: 10.1109/TNNLS.2021.3082528 – ident: ref30 doi: 10.1109/TAC.2017.2648041 – ident: ref6 doi: 10.1109/TSMC.2019.2898551 – ident: ref39 doi: 10.1109/81.995659 – ident: ref9 doi: 10.1109/TAC.2008.2009515 – ident: ref33 doi: 10.1109/TAC.2020.3034869 – ident: ref41 doi: 10.1016/j.neunet.2019.02.002 – ident: ref24 doi: 10.1109/TSMC.2019.2933005 – ident: ref21 doi: 10.1016/j.automatica.2021.109899 – ident: ref23 doi: 10.1109/TCYB.2017.2766762 – ident: ref34 doi: 10.1109/TNNLS.2016.2524619 – ident: ref2 doi: 10.1109/TCYB.2019.2948424 – ident: ref48 doi: 10.1109/TCSI.2004.830694 – ident: ref37 doi: 10.1109/TCYB.2021.3102941 – ident: ref5 doi: 10.1016/j.automatica.2018.05.013 – ident: ref14 doi: 10.1109/TCYB.2017.2760908 – ident: ref3 doi: 10.1109/TSMC.2019.2931846 – ident: ref4 doi: 10.1109/TAC.2015.2449811 – ident: ref46 doi: 10.1007/978-3-540-72395-0_25 |
| SSID | ssj0001286306 |
| Score | 2.3640091 |
| Snippet | In this article, we present a collaborative neurodynamic approach to distributed optimization with nonconvex functions. We develop a recurrent neural network... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 3141 |
| SubjectTerms | Collaboration Collaborative neurodynamic optimization (CNO) Convergence distributed optimization Global optimization Heuristic methods Linear programming Metaheuristics Neurodynamics nonconvex functions Optimization Recurrent neural networks recurrent neural networks (RNNs) |
| Title | A Collaborative Neurodynamic Approach to Distributed Global Optimization |
| URI | https://ieeexplore.ieee.org/document/9963658 https://www.proquest.com/docview/2800962977 |
| Volume | 53 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV27TsMwFL0qnWDgVRCFgjwwIdI6jhs7Y1WoKqTCQCt1ixzbWYAWQcrA13OduKU8hFiiKLIly_fGPsePcwDOc5sYoYUKoliagHMrg0zbLAiVykQ3jIzI3Trk6DYeTvjNtDutweXqLoy1tjx8ZtvutdzLN3O9cEtlHcTmEc6YG7CBxK26q7W2niLjqLTSZGGMwcen38QMadIZ34_6SAYZa2MCI2YRX6ah0lflx2BczjCDHRgt21YdLHloL4qsrd-_yTb-t_G7sO2hJulVubEHNTvbh601AcIGDHuk_5kIb5aUWh2mcqknPa83Too5uXICu84byxpS-QSQOxxtnvw1zgOYDK7H_WHgvRUCjRN8EeSh1jwXkisnkEYjpHlScWkEpTrDz5xGAqmaVIpxoRC0WaQ-iAWlstRZZUSHUJ_NZ_YISBwjRUyUQfDGORNMhVmcG00Nj3XIDGsCXXZ1qr3wuPO_eExLAkKT1EUnddFJfXSacLGq8lypbvxVuOF6e1XQd3QTWst4pv6_fE2ZdJyNIeg9_r3WCWw6Q_nqSGML6sXLwp4i7CiyszLfPgAFbtF1 |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV07T8MwED5BGYCBN6I8PTAh0jqOGztjVajKozBQJLbIsZ0FaBGkDPx6zolbnkIsURTZkuW7-L6zz98HcJjbxAgtVBDF0gScWxlk2mZBqFQmWmFkRO72IftXce-Wn9-17mbgeHoXxlpbFp_Zhnstz_LNSI_dVlkTsXmEEXMW5jDut8LqttanHRUZR6WYJgtjND8-_TFmSJPm4KbfwXSQsQa6MKIW8SUQlcoqP5bjMsZ0l6E_GV1VWnLfGBdZQ799I2787_BXYMmDTdKuvGMVZuxwDRY_URCuQ69NOh-u8GpJydZhKp160vaM46QYkRNHsevUsawhlVIAucb15tFf5NyA2-7poNMLvLpCoDHEF0Eeas1zIblyFGk0wkRPKi6NoFRn-JnTSGCyJpViXCiEbRaTH0SDUlnqxDKiTagNR0O7BSSOMUlMlEH4xjkTTIVZnBtNDY91yAyrA51Mdao99bhTwHhIyxSEJqmzTuqsk3rr1OFo2uWp4t34q_G6m-1pQz_Rddid2DP1f-ZLyqTL2hjC3u3fex3AfG_Qv0wvz64udmDByctXBY67UCuex3YPQUiR7Ze-9w4iM9S- |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Collaborative+Neurodynamic+Approach+to+Distributed+Global+Optimization&rft.jtitle=IEEE+transactions+on+systems%2C+man%2C+and+cybernetics.+Systems&rft.au=Xia%2C+Zicong&rft.au=Liu%2C+Yang&rft.au=Wang%2C+Jun&rft.date=2023-05-01&rft.issn=2168-2216&rft.eissn=2168-2232&rft.volume=53&rft.issue=5&rft.spage=3141&rft.epage=3151&rft_id=info:doi/10.1109%2FTSMC.2022.3221937&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TSMC_2022_3221937 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2216&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2216&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2216&client=summon |