Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of <inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula> -gre...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE access Vol. 9; pp. 65650 - 65659
Main Authors:	Liu, Xiaofei, Zhang, Pei, Fang, Hui, Zhou, Yinglu
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Archives & records Genetic algorithms Heuristic algorithms Iterative methods Linear programming Multi-objective reactive power optimization Multiple objective analysis Optimization Pareto archive algorithm Pareto optimization Pareto optimum Particle swarm optimization Power system stability Reactive power Sorting algorithms voltage control ε-<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">greedy strategy
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of <inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula> -greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II).
AbstractList	This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of <inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula> -greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II). This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of <tex-math notation="LaTeX">$\varepsilon $ </tex-math>-greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II). This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of [Formula Omitted] -greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II).
Author	Liu, Xiaofei Fang, Hui Zhang, Pei Zhou, Yinglu
Author_xml	– sequence: 1 givenname: Xiaofei orcidid: 0000-0003-2467-8409 surname: Liu fullname: Liu, Xiaofei organization: School of Electrical Engineering, Beijing Jiaotong University, Beijing, China – sequence: 2 givenname: Pei orcidid: 0000-0001-9706-2854 surname: Zhang fullname: Zhang, Pei email: peizhang166@qq.com organization: School of Electrical Engineering, Beijing Jiaotong University, Beijing, China – sequence: 3 givenname: Hui surname: Fang fullname: Fang, Hui organization: State Grid Chongqing Electric Power Research Institute, Chongqing, China – sequence: 4 givenname: Yinglu surname: Zhou fullname: Zhou, Yinglu organization: State Grid Chongqing Electric Power Company, Chongqing, China
BookMark	eNp9UdFu0zAUtdCQGNu-YC-ReE6xncSOH0s1tkpDndYhHi3bue5cJXFx3E3lI_gbfoNvwm0KAh5mWfLR1TnnXt_zFp30vgeELgmeEILF--lsdrVcTiimZFJgXnHOX6FTSpjIi6pgJ3_hN-hiGNY4nTqVKn6Kvn_attHlC70GE90TZPegRnDnnyFki010nfumovN99kEN0GQJzLtN8E8J36kQnWkhWz6r0P3L_uLiY_bzR34dAJpdtoxBRVjtMtUfdBB9Ng3mcd9r2q58SPTuHL22qh3g4vieoc8frx5mN_nt4no-m97mpsR1zEtWlaCZ4bbBRNSEakLAmsIoooXQYFNHqDnTYAQtS1EQi4UA3DADVBtdnKH56Nt4tZab4DoVdtIrJw8FH1by-DNpmdK6xoRhi0uLqeaMaVJRTmzNFK-T17vRK-3k6xaGKNd-G_o0vqQVpZRwLGhiiZFlgh-GAFYaFw-LSntxrSRY7tOUY5pyn6Y8ppm0xX_a3xO_rLocVQ4A_ihESVi6xS_yr7Bd
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1049_rpg2_12868 crossref_primary_10_1002_jnm_70013 crossref_primary_10_1109_ACCESS_2024_3426104 crossref_primary_10_3390_pr13082455 crossref_primary_10_1016_j_energy_2022_125635 crossref_primary_10_3390_pr10071401 crossref_primary_10_3390_en17092001 crossref_primary_10_1016_j_asoc_2024_111548 crossref_primary_10_1016_j_swevo_2025_102006 crossref_primary_10_3390_act14010009 crossref_primary_10_3390_en17246454 crossref_primary_10_3390_en17112774 crossref_primary_10_1002_jnm_70085 crossref_primary_10_1088_1742_6596_2833_1_012017 crossref_primary_10_1109_TIV_2024_3352581 crossref_primary_10_1186_s42162_024_00341_3 crossref_primary_10_1109_TCAD_2024_3485575 crossref_primary_10_1002_jnm_3204 crossref_primary_10_1007_s10586_024_04542_z crossref_primary_10_1109_ACCESS_2024_3445143 crossref_primary_10_1016_j_enbenv_2022_12_001 crossref_primary_10_1109_ACCESS_2022_3158666 crossref_primary_10_1016_j_rser_2024_114282 crossref_primary_10_1109_ACCESS_2023_3289856 crossref_primary_10_1109_TIA_2024_3379489
Cites_doi	10.1007/BFb0040811 10.1109/TPWRS.2019.2941134 10.1016/j.ijepes.2008.04.005 10.1016/j.ijepes.2012.05.032 10.1109/ICEMS.2017.8055969 10.1109/SIS.2003.1202264 10.3390/en9020107 10.1016/j.ijepes.2016.03.024 10.1109/ICNN.1995.488968 10.1016/j.asoc.2020.106516 10.1109/TSG.2019.2962625 10.1109/ICITSI.2016.7858224 10.1016/j.cor.2004.08.012 10.1007/978-3-540-31989-4_26 10.1155/2015/754582 10.1016/j.asoc.2012.01.006 10.1007/s13369-012-0297-z 10.1109/APPEEC.2016.7779630 10.1049/iet-gtd.2014.0469 10.1109/ACCESS.2020.2974961 10.1007/s10489-018-1319-7 10.1109/ICEC.1998.699146 10.1109/TSG.2014.2329842 10.1109/ACCESS.2021.3056423 10.1109/4235.996017 10.1109/ACCESS.2018.2852941 10.1109/TEVC.2007.892759 10.1162/evco.1994.2.3.221 10.1109/ACCESS.2020.3018484 10.1109/CEC.2004.1330905 10.1109/TPWRS.2010.2051168 10.1109/ACCESS.2021.3053640 10.3390/en13225871 10.1109/CEC.1999.781913 10.1109/4235.797969 10.1109/TSG.2013.2256938 10.1016/j.ijepes.2016.03.007
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2021.3075777
DatabaseName	IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	65659
ExternalDocumentID	oai_doaj_org_article_f6abb80160f04f02b766b15271f86a78 10_1109_ACCESS_2021_3075777 9416416
Genre	orig-research
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c408t-4654eb6c7fd019812b11efc3ca1b99befeede876bec9244931f099e0d6ce2bcb3
IEDL.DBID	RIE
ISICitedReferencesCount	28
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000647306500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:53:19 EDT 2025 Sun Jun 29 15:31:21 EDT 2025 Sat Nov 29 06:12:12 EST 2025 Tue Nov 18 22:13:27 EST 2025 Wed Aug 27 02:40:05 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c408t-4654eb6c7fd019812b11efc3ca1b99befeede876bec9244931f099e0d6ce2bcb3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-9706-2854 0000-0003-2467-8409
OpenAccessLink	https://ieeexplore.ieee.org/document/9416416
PQID	2522217092
PQPubID	4845423
PageCount	10
ParticipantIDs	crossref_citationtrail_10_1109_ACCESS_2021_3075777 proquest_journals_2522217092 ieee_primary_9416416 doaj_primary_oai_doaj_org_article_f6abb80160f04f02b766b15271f86a78 crossref_primary_10_1109_ACCESS_2021_3075777
PublicationCentury	2000
PublicationDate	20210000 2021-00-00 20210101 2021-01-01
PublicationDateYYYYMMDD	2021-01-01
PublicationDate_xml	– year: 2021 text: 20210000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2021
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 ref37 ref15 soldevilla (ref29) 2019 ref36 ref14 ref31 ref33 zhou (ref10) 2020; 95 ref11 ref32 ref2 guo (ref44) 2018 ref1 ref38 ref19 ref18 eberhart (ref35) 2002 shi (ref34) 1998 trivedi (ref17) 2017; 21 (ref46) 2019 ref24 ref45 ref23 ref26 ref20 ref42 ref41 ref22 huang (ref30) 2019 ref21 ref43 zhang (ref39) 2012; 36 ref28 ref27 ref8 ref7 ref9 ref4 ref3 ref6 ref5 jeyanthy (ref25) 2010; 2 ref40 li (ref16) 2007; 11
References_xml	– ident: ref32 doi: 10.1007/BFb0040811 – ident: ref6 doi: 10.1109/TPWRS.2019.2941134 – ident: ref22 doi: 10.1016/j.ijepes.2008.04.005 – ident: ref23 doi: 10.1016/j.ijepes.2012.05.032 – volume: 2 start-page: 2715 year: 2010 ident: ref25 article-title: Multi-objective genetic algorithm for reactive power optimization inclusing voltage stabilityy publication-title: Int J Eng Sci Technol – ident: ref31 doi: 10.1109/ICEMS.2017.8055969 – year: 2019 ident: ref46 publication-title: Matpower – volume: 36 start-page: 158 year: 2012 ident: ref39 article-title: Reactive power optimization for medium voltage distribution network based on improved particle swarm optimization publication-title: Power Syst Technol – start-page: 591 year: 1998 ident: ref34 article-title: Parameter selection in particle swarm optimization publication-title: Proc Int Conf Evol Program – ident: ref38 doi: 10.1109/SIS.2003.1202264 – ident: ref13 doi: 10.3390/en9020107 – ident: ref9 doi: 10.1016/j.ijepes.2016.03.024 – ident: ref43 doi: 10.1109/ICNN.1995.488968 – start-page: 189 year: 2019 ident: ref30 article-title: Reactive power optimization for distribution network based on improved bacterial chemotaxis particle swarm optimization publication-title: Proc Int Symp Comput Intell Design – volume: 95 start-page: 1 year: 2020 ident: ref10 article-title: Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2020.106516 – ident: ref5 doi: 10.1109/TSG.2019.2962625 – ident: ref20 doi: 10.1109/ICITSI.2016.7858224 – ident: ref36 doi: 10.1016/j.cor.2004.08.012 – ident: ref41 doi: 10.1007/978-3-540-31989-4_26 – ident: ref40 doi: 10.1155/2015/754582 – ident: ref8 doi: 10.1016/j.asoc.2012.01.006 – ident: ref27 doi: 10.1007/s13369-012-0297-z – ident: ref24 doi: 10.1109/APPEEC.2016.7779630 – ident: ref28 doi: 10.1049/iet-gtd.2014.0469 – start-page: 1 year: 2019 ident: ref29 article-title: Minimization of losses in power systems by reactive power dispatch using particle swarm optimization publication-title: Proc Int Univ Power Eng Conf – ident: ref7 doi: 10.1109/ACCESS.2020.2974961 – ident: ref18 doi: 10.1007/s10489-018-1319-7 – ident: ref33 doi: 10.1109/ICEC.1998.699146 – ident: ref12 doi: 10.1109/TSG.2014.2329842 – ident: ref3 doi: 10.1109/ACCESS.2021.3056423 – ident: ref15 doi: 10.1109/4235.996017 – ident: ref4 doi: 10.1109/ACCESS.2018.2852941 – start-page: 28 year: 2018 ident: ref44 publication-title: Deep Understanding Reinforcement Learning An Introduction to Principles – volume: 21 start-page: 440 year: 2017 ident: ref17 article-title: A survey of multiobjective evolutionary algorithms based on decomposition publication-title: IEEE Trans Evol Comput – volume: 11 start-page: 712 year: 2007 ident: ref16 article-title: MOEA/D: A multi-objective evolutionary algorithm based on decomposition publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2007.892759 – ident: ref14 doi: 10.1162/evco.1994.2.3.221 – ident: ref42 doi: 10.1109/ACCESS.2020.3018484 – ident: ref37 doi: 10.1109/CEC.2004.1330905 – start-page: 84 year: 2002 ident: ref35 article-title: Comparing inertia weights and constriction factors in particle swarm optimization publication-title: Proc Congr Evol Comput – ident: ref45 doi: 10.1109/TPWRS.2010.2051168 – ident: ref2 doi: 10.1109/ACCESS.2021.3053640 – ident: ref11 doi: 10.3390/en13225871 – ident: ref21 doi: 10.1109/CEC.1999.781913 – ident: ref19 doi: 10.1109/4235.797969 – ident: ref1 doi: 10.1109/TSG.2013.2256938 – ident: ref26 doi: 10.1016/j.ijepes.2016.03.007
SSID	ssj0000816957
Score	2.3473418
Snippet	This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	65650
SubjectTerms	Algorithms Archives & records Genetic algorithms Heuristic algorithms Iterative methods Linear programming Multi-objective reactive power optimization Multiple objective analysis Optimization Pareto archive algorithm Pareto optimization Pareto optimum Particle swarm optimization Power system stability Reactive power Sorting algorithms voltage control ε-<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">greedy strategy
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3JTsMwELUQ4gAHxCrKJh84ErCTNI6PbUXFAUHFIrhZtmOziLaoBBA_wd_wG3wT44WqCAkuSDlY0TiJPZNZLPs9hHYKzSEJMVUiSSWTHGJWwq0hoBCqZcZzo5VnLTlix8fl1RXvTVB9uT1hAR44TNy-LaRSpcNBsyS3JFWsKJTjYqW2LCTzx3wJ4xPFlPfBJS14k0WYIUr4fqvTgRFBQZjSPbDrJmPsWyjyiP2RYuWHX_bBpruA5mOWiFvh6xbRlBksobkJ7MBl9OaPziYn6i64LHxqZGj0HPEZPgFf0I-HLHEbYlWFoRHWEKDdiyPHZy9y1P8ufXlb3-CP98TtyalecQSwfcVy4PuZeogjYC1u3V8PRyDeX0EX3YPzzmESyRUSnZOyThyOmlGFZraCLA_CvKLUWJ1pSRXnylh4gwFXCTqGEi3nGbWQTBpSOQoxpVW2iqYHw4FZQ7gJJWFR5lTRDPKR0kgowiqWaecrQPusgdKveRY6Io87Aox74SsQwkVQjnDKEVE5DbQ77vQQgDd-F287BY5FHWq2vwG2JOKMir9sqYGWnfrHD-GQrcLVQJtf5iDiH_4oUkhcoZwjPF3_j1dvoFk3nLC4s4mm69GT2UIz-rm-fRxte-P-BO3T_EE priority: 102 providerName: Directory of Open Access Journals
Title	Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm
URI	https://ieeexplore.ieee.org/document/9416416 https://www.proquest.com/docview/2522217092 https://doaj.org/article/f6abb80160f04f02b766b15271f86a78
Volume	9
WOSCitedRecordID	wos000647306500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NTtwwEB4B6qE99A-qLqXIhx4J2Ek2jo_LCtRDCysoKjfLdsZAxe5WS6Di0kfo2_Q1-kwdOyYqoqpUKYqsyE6cfKPxzMTzDcC7yikyQrDJDG9MVtKalSmPnAARzhSqRGdj1ZIP8uCgPj1VkyXY6nNhEDFuPsPt0Iz_8pu5uw6hsh1F1gMdy7AsZdXlavXxlFBAQg1lIhYSXO2MxmN6B3IBc7FNkjyUUt5bfCJHfyqq8kATx-Vl_9n_Tew5PE1mJBt1uL-AJZy9hCd_kAuuwo-YW5sd2i-dTmNHaLrGJFRGY4ekLKYpC5Pt0mLWMGp0QQZqT5JMseNvZjG93_vzRXvOfv3Mwqad5pYlhttbZmZxHLZzlhht2ejybL6g7tM1ONnf-zR-n6XqC5kred1mgWgNbeWkb8gMJDvACoHeFc4Iq5RFT09A0qUkBOTDlaoQnqxN5E2oMWadLV7Bymw-w9fAhuQzVnUprCjIYKnRkJfWyMIFZULiIQeQ38GiXaImDxUyLnV0UbjSHZY6YKkTlgPY6gd97Zg5_t19N-Dddw202vECAanTF9W-MtbWgXTP89Lz3MqqsqHwr_B1ZWQ9gNUAfn-ThPsANu6kRycVcKVzsmzJ3-MqX__7qDfwOEywi-dswEq7uMa38MjdtBdXi80YHKDzx-97m1HSfwPVfPwq
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NbtQwEB6VggQc-GsRCwV84Ni0dpKN4-N2RVXEsl1BEb1ZtjOGou4u2qagvgRvw2vwTIwdN6ICISHlYEV24uQbjWcm8fcBvKicoiAEm8zwxmQlrVmZ8sgJEOFMoUp0NqqWTOR0Wh8fq9kabPd7YRAx_nyGO6EZv-U3S3ceSmW7iqIHOq7B9aCclXZr9RWVICGhhjJRCwmudkfjMT0FJYG52CFbHkopryw_kaU_yar84YvjArN_9_-mdg_upECSjTrk78MaLh7A7d_oBTfge9xdmx3az51XY2_RdI1Z0EZjh-Qu5mkfJtuj5axh1OjKDNSeJati776Z1fxq7w8n7Sf280cWfttpLljiuL1gZhHHYbtkidOWjU4_LlfUfb4J7_dfHo0PsqS_kLmS120WqNbQVk76hgJBigSsEOhd4YywSln0dAckb0pmQFlcqQrhKd5E3gSVMets8RDWF8sFPgI2pKyxqkthRUEhS42G8rRGFi64EzIQOYD8EhbtEjl50Mg41TFJ4Up3WOqApU5YDmC7H_Sl4-b4d_e9gHffNRBrxxMEpE5vVPvKWFsH2j3PS89zK6vKBulf4evKyHoAGwH8_iIJ9wFsXVqPTk7gTOcU21LGx1X--O-jnsPNg6M3Ez15NX39BG6FyXbVnS1Yb1fn-BRuuK_tydnqWbT0XzT2_U0
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=Multi-Objective+Reactive+Power+Optimization+Based+on+Improved+Particle+Swarm+Optimization+With+%CE%B5-Greedy+Strategy+and+Pareto+Archive+Algorithm&rft.jtitle=IEEE+access&rft.au=Liu%2C+Xiaofei&rft.au=Zhang%2C+Pei&rft.au=Fang%2C+Hui&rft.au=Zhou%2C+Yinglu&rft.date=2021&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=9&rft.spage=65650&rft.epage=65659&rft_id=info:doi/10.1109%2FACCESS.2021.3075777&rft.externalDocID=9416416
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon