An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weak...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE access Vol. 10; p. 1
Main Authors:	Zhang, Xuan-Yu, Zhou, Kai-Qing, Li, Peng-Cheng, Xiang, Yin-Hong, Zain, Azlan Mohd, Sarkheyli-Hagele, Arezoo
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive algorithms Adaptive weighting modification Ant colony optimization Chaos Convergence cubic chaos mapping levy flight Optimization Optimization algorithms reverse learning Search algorithms Search problems Sociology Sparrow search algorithm Statistical analysis Statistics Swarm intelligence
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.
AbstractList	Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum. Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.
Author	Sarkheyli-Hagele, Arezoo Xiang, Yin-Hong Zhou, Kai-Qing Zhang, Xuan-Yu Li, Peng-Cheng Zain, Azlan Mohd
Author_xml	– sequence: 1 givenname: Xuan-Yu surname: Zhang fullname: Zhang, Xuan-Yu organization: School of Information Science and Engineering, Jishou University, Jishou, Hunan, China – sequence: 2 givenname: Kai-Qing surname: Zhou fullname: Zhou, Kai-Qing organization: School of Information Science and Engineering, Jishou University, Jishou, Hunan, China – sequence: 3 givenname: Peng-Cheng surname: Li fullname: Li, Peng-Cheng organization: School of Information Science and Engineering, Jishou University, Jishou, Hunan, China – sequence: 4 givenname: Yin-Hong surname: Xiang fullname: Xiang, Yin-Hong organization: School of Information Science and Engineering, Jishou University, Jishou, Hunan, China – sequence: 5 givenname: Azlan Mohd orcidid: 0000-0003-2004-3289 surname: Zain fullname: Zain, Azlan Mohd organization: UTM Big Data Center, University Teknologi Malaysia, Skudai, Johor, Malaysia – sequence: 6 givenname: Arezoo surname: Sarkheyli-Hagele fullname: Sarkheyli-Hagele, Arezoo organization: Department of Computer Science and Media Technology, Internet of Things and People Research Center, Malmö University, Malmö, Sweden
BackLink	https://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-55407$$DView record from Swedish Publication Index
BookMark	eNqFkU9v0zAYxiM0JMbYJ9jFElda_Ld2jlEYrNLQDmVwtGzndeqqiYOTbhqfHreZJuCCD7b1-nl-tt_nbXHWxx6K4orgJSG4_FjV9fVms6SY0iWjmMtSvSrOKVmVCybY6uyP_Zvichx3OA-VS0KeF0PVo3U3pPgADaq3Jo5oM5iU4iPagElui-6GKXThl5lC7FG1b2MK07ZD92PoW1Q1Jh8_APoBod1O6Gtsgg9uFpu-QTdPNoUGbaZkJmgDjO-K197sR7h8Xi-K-8_X3-qbxe3dl3Vd3S4c52JaOMXAutJb3HjslHJeOuo9GG4p9paViljhrOTOee6ldM4SBkAcNZR5IdhFsZ65TTQ7PaTQmfSkown6VIip1SZNwe1BG1WCs4JJrihXNs9EKSq8pA2xBsrM-jCzxkcYDvYv2qfwvTrROnPQQnAss_z9LM9t_XmAcdK7eEh9_q2mkqwYl0wdH8hmlUtxHBP4FyzB-hisnoPVx2D1c7DZVf7jcmE6tTt3OOz_472avQEAXm4rlVSMCvYbWY21Kw
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1007_s11760_024_03144_x crossref_primary_10_3389_fnbot_2023_1190977 crossref_primary_10_3390_s23177513 crossref_primary_10_1016_j_cie_2023_109425 crossref_primary_10_1109_ACCESS_2024_3402652 crossref_primary_10_3390_electronics13142839 crossref_primary_10_1371_journal_pone_0297380 crossref_primary_10_1109_ACCESS_2023_3275010 crossref_primary_10_3390_app14156626 crossref_primary_10_1007_s13042_024_02227_y crossref_primary_10_1177_14759217241271044 crossref_primary_10_3390_s24134333 crossref_primary_10_1016_j_mtcomm_2024_110327 crossref_primary_10_1016_j_geog_2025_03_002 crossref_primary_10_1016_j_infrared_2024_105688 crossref_primary_10_3389_fpls_2024_1354290 crossref_primary_10_1007_s12206_024_0607_x crossref_primary_10_1080_15325008_2023_2298280 crossref_primary_10_3390_su15042944 crossref_primary_10_3390_math11194037 crossref_primary_10_1109_ACCESS_2024_3449998 crossref_primary_10_1109_JSEN_2024_3405940 crossref_primary_10_1016_j_compgeo_2023_106036 crossref_primary_10_1108_RS_10_2023_0037 crossref_primary_10_3390_electronics13193951
Cites_doi	10.3390/s21041224 10.1016/j.eswa.2021.115637 10.1016/j.ins.2021.02.024 10.1007/s00500-022-06741-5 10.1109/ACCESS.2021.3130640 10.1155/2021/3946958 10.1007/s11721-007-0002-0 10.1007/s10489-021-02972-5 10.1177/0954411920987964 10.1007/s11042-016-3907-z 10.3390/electronics11050704 10.1007/s11721-007-0009-6 10.1016/j.knosys.2021.106924 10.1155/2021/6622935 10.1504/IJCAT.2021.121524 10.1016/j.knosys.2022.108626 10.1007/s11721-008-0022-4 10.1109/ACCESS.2021.3075547 10.1007/978-3-319-93025-1_3 10.1155/2021/5556780 10.1016/j.ijhydene.2020.12.107 10.1587/transinf.E96.D.2309 10.1016/j.advengsoft.2013.12.007 10.1016/j.asoc.2014.06.034 10.1142/S0218127402005492 10.1016/j.advengsoft.2016.01.008 10.1080/21642583.2019.1708830 10.1109/ACCESS.2021.3128433 10.1016/j.compind.2019.06.004 10.3390/s20051420 10.1109/ACCESS.2021.3052960 10.1016/j.eswa.2021.116158 10.3390/su13094896
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D ADTPV AOWAS D8T ZZAVC DOA
DOI	10.1109/ACCESS.2022.3204798
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 SwePub SwePub Articles SWEPUB Freely available online SwePub Articles full text 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: DOAJ 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 Statistics
EISSN	2169-3536
EndPage	1
ExternalDocumentID	oai_doaj_org_article_a89ecb53748248b48218825f72d1bae9 oai_DiVA_org_mau_55407 10_1109_ACCESS_2022_3204798 9878325
Genre	orig-research
GrantInformation_xml	– fundername: Jishou University Graduate Research and Innovation Project grantid: JDY21067 – fundername: National Natural Science Foundation of China grantid: No. 62066016 funderid: 10.13039/501100001809 – fundername: Natural Science Foundation of Hunan Province, China grantid: 2020JJ5458
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR 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 ABAZT ADTPV AOWAS D8T ZZAVC
ID	FETCH-LOGICAL-c445t-c83ebc9fb0df0c88cf7c2ffea4b20fb3981b5cb74ccf4f77ccb13ee1c2a23f553
IEDL.DBID	DOA
ISICitedReferencesCount	26
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000857703700001&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	Mon Dec 08 04:16:39 EST 2025 Thu Aug 21 06:58:39 EDT 2025 Sun Nov 30 05:23:37 EST 2025 Sat Nov 29 06:32:22 EST 2025 Tue Nov 18 20:53:17 EST 2025 Tue Nov 25 14:44:28 EST 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-c445t-c83ebc9fb0df0c88cf7c2ffea4b20fb3981b5cb74ccf4f77ccb13ee1c2a23f553
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-2004-3289
OpenAccessLink	https://doaj.org/article/a89ecb53748248b48218825f72d1bae9
PQID	2716347385
PQPubID	4845423
PageCount	1
ParticipantIDs	proquest_journals_2716347385 swepub_primary_oai_DiVA_org_mau_55407 crossref_citationtrail_10_1109_ACCESS_2022_3204798 doaj_primary_oai_doaj_org_article_a89ecb53748248b48218825f72d1bae9 crossref_primary_10_1109_ACCESS_2022_3204798 ieee_primary_9878325
PublicationCentury	2000
PublicationDate	20220000 2022-00-00 20220101 2022 2022-01-01
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– year: 2022 text: 20220000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2022
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 ref35 ref12 ref34 ref15 ref37 ref14 Mao (ref24) 2021; 15 ref36 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref16 ref19 Lv (ref9) 2021; 43 ref18 Lv (ref23) 2020; 12 Andi (ref8) 2021; 41 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref7 ref4 ref3 ref6 ref5
References_xml	– ident: ref25 doi: 10.3390/s21041224 – ident: ref18 doi: 10.1016/j.eswa.2021.115637 – ident: ref19 doi: 10.1016/j.ins.2021.02.024 – ident: ref21 doi: 10.1007/s00500-022-06741-5 – ident: ref36 doi: 10.1109/ACCESS.2021.3130640 – ident: ref13 doi: 10.1155/2021/3946958 – ident: ref28 doi: 10.1007/s11721-007-0002-0 – ident: ref22 doi: 10.1007/s10489-021-02972-5 – ident: ref7 doi: 10.1177/0954411920987964 – ident: ref34 doi: 10.1007/s11042-016-3907-z – ident: ref1 doi: 10.3390/electronics11050704 – ident: ref2 doi: 10.1007/s11721-007-0009-6 – ident: ref12 doi: 10.1016/j.knosys.2021.106924 – ident: ref15 doi: 10.1155/2021/6622935 – volume: 15 start-page: 1155 issue: 6 year: 2021 ident: ref24 article-title: Improved sparrow algorithm combining Cauchy mutation and opposition-based learning publication-title: J. Frontiers Comput. Sci. Technol. – ident: ref35 doi: 10.1504/IJCAT.2021.121524 – ident: ref17 doi: 10.1016/j.knosys.2022.108626 – ident: ref32 doi: 10.1007/s11721-008-0022-4 – ident: ref10 doi: 10.1109/ACCESS.2021.3075547 – volume: 43 start-page: 318 issue: 2 year: 2021 ident: ref9 article-title: Multi-threshold image segmentation based on improved sparrow search algorithm publication-title: Syst. Eng. Electron. – ident: ref30 doi: 10.1007/978-3-319-93025-1_3 – ident: ref14 doi: 10.1155/2021/5556780 – volume: 12 start-page: 1 year: 2020 ident: ref23 article-title: Chaos sparrow search optimization algorithm publication-title: J. Beijing Univ. Aeronaut. Astronaut. – ident: ref6 doi: 10.1016/j.ijhydene.2020.12.107 – ident: ref31 doi: 10.1587/transinf.E96.D.2309 – ident: ref27 doi: 10.1016/j.advengsoft.2013.12.007 – ident: ref37 doi: 10.1016/j.asoc.2014.06.034 – ident: ref33 doi: 10.1142/S0218127402005492 – ident: ref29 doi: 10.1016/j.advengsoft.2016.01.008 – ident: ref5 doi: 10.1080/21642583.2019.1708830 – ident: ref16 doi: 10.1109/ACCESS.2021.3128433 – ident: ref20 doi: 10.1016/j.compind.2019.06.004 – ident: ref4 doi: 10.3390/s20051420 – ident: ref26 doi: 10.1109/ACCESS.2021.3052960 – ident: ref3 doi: 10.1016/j.eswa.2021.116158 – volume: 41 start-page: 2128 issue: 7 year: 2021 ident: ref8 article-title: Path planning method of unmanned aerial vehicle based on chaos sparrow search algorithm publication-title: J. Comput. Appl. – ident: ref11 doi: 10.3390/su13094896
SSID	ssj0000816957
Score	2.372036
Snippet	Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique...
SourceID	doaj swepub proquest crossref ieee
SourceType	Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	1
SubjectTerms	Adaptive algorithms Adaptive weighting modification Ant colony optimization Chaos Convergence cubic chaos mapping levy flight Optimization Optimization algorithms reverse learning Search algorithms Search problems Sociology Sparrow search algorithm Statistical analysis Statistics Swarm intelligence
SummonAdditionalLinks	– databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB61FYdy4NGCCBTkA9yaNnHs2DmGhaoHKEhA6c3ys63U3az2gcS_x6-NuhJC4hJFkePE-sb2zHjmG4C3mLeKa1mVtXa0JJayUmHnStnWhrYGcxprRl5-YhcX_Oqq-7oDx2MujLU2Bp_Zk3Abz_LNoNfBVXbq7WMvgHQXdhlrU67W6E8JBSQ6yjKxUF11p_1k4sfgTUCMTxocqNT51uYTOfpzUZVt_fI-Z2jcZ84e_98fPoFHWZ9EfRKAp7BjZwfw8B7L4AHsB4Uy8TEfwryfoeRIsAZNbuSwRN_mkYgRpchj9MUvItOcnYn6u-thcbu6maIYW4B6I-dhgUQ_o0sVfR5MiDVKjeXMoPPfIQUMbUhv7fIZ_Dj7-H1yXuaqC6UmhK5KzRurdOdUZVylOdeOaY-elUThyqmm84ou1YoRrR1xjGmt6sbaWmOJG0dp8xz2ZsPMvgBksZcD4pzfHTlRrZQeNmrCQSVhXi9gBeANHEJnSvJQGeNORNOk6kTCUAQMRcawgOPxpXli5Ph38_cB57FpoNOODzxuIs9OIXlntaKBigcTrvy19pYHdQybWknbFXAYsB47yTAXcLSRGpGn_lJgb4E2JJAEFfAuSdLWxz_cXvbx41O5FjTwH778e--vYD8MJPl7jmBvtVjb1_BA__Iis3gTpf8PteAE2w priority: 102 providerName: IEEE
Title	An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies
URI	https://ieeexplore.ieee.org/document/9878325 https://www.proquest.com/docview/2716347385 https://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-55407 https://doaj.org/article/a89ecb53748248b48218825f72d1bae9
Volume	10
WOSCitedRecordID	wos000857703700001&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: DOAJ 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/eLvHCXMwrV1LT9wwELYQ4kAPFY9WDVDkQ3trIHHs2D6mC4gD0EotlJvlZ1mJza52FyQu_e31a1e7l3LpJYfIiR_fZMbjzHwDwCfEWsW0rMpaO1JiS2ipkHOlbGtDWoMYiTUj767ozQ27v-ffV0p9hZiwRA-cFu5UMm61IoElBWGm_LX2m0LiKDK1kjam7lWUrzhTUQezuuWEZpqhuuKn3WDgZ-QdQoROGhSI1dmaKYqM_bnEyvpuc5VBNFqdix3wNm8XYZeGuQs2bL8H3qyQCO6DSdfDdDRgDRw8yPEM_phEakWYYonhN68WRjnfEnaPv8fT4fxhBGO0AOyMnASVB3_FQ1J4PTYheig1lr2Bly8hqQsuaGzt7B24vTj_Obgscx2FUmNM5qVmjVWaO1UZV2nGtKPa42ElVqhyquF-60q0olhrhx2lWqu6sbbWSKLGEdK8B5v9uLcfALTII4ud8_aOYdVKSWlLTPj1iKm39LQAaLGkQmeS8VDr4lFEZ6PiIuEgAg4i41CAL8uHJolj49_Nvwaslk0DQXa84cVGZLERr4lNAfYD0suXcEa9biMFOFogL_LHPBPI-5QNDrQ_BficpGGt87PhXRc7H8knQQKj4cH_GOIh2A7TTuc9R2BzPn2yH8GWfp4PZ9PjKO_-ev3n_DhmLf4FWGEFCw
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgkQ58GhBBAr4ALemTRw7do5hoVrEdkGilN4s27FppW6y2gcS_x7b8UZdCSFxiaLIcWJ9Y3tmPPMNwFvMS8W1zNJcW5oSQ1mqsLWpLPOGlg3mNNSMvJiw6ZRfXlZfd-BoyIUxxoTgM3Psb8NZftPptXeVnTj72AkgvQN3KSE467O1Bo-KLyFRURaphfKsOqlHIzcKZwRifFxgT6bOt7afwNIfy6psa5i3WUPDTnP66P_-8TE8jBolqnsReAI7pt2HB7d4Bvdhz6uUPSPzAczrFvWuBNOg0ZXslujbPFAxoj72GH1xy8gs5mei-uZnt7heXc1QiC5AdSPnfolEP4JTFZ11jY826hvLtkHj3z4JDG1ob83yKXw__Xg-Gqex7kKqCaGrVPPCKF1ZlTU205xry7TDz0iicGZVUTlVl2rFiNaWWMa0VnlhTK6xxIWltHgGu23XmueADHaSQKx1-yMnqpSSsZI2_qiSMKcZsATwBg6hIym5r41xI4JxklWix1B4DEXEMIGj4aV5z8nx7-bvPc5DU0-oHR443EScn0LyymhFPRkPJly5a-5sD2oZbnIlTZXAgcd66CTCnMDhRmpEnPxLgZ0NWhBPE5TAu16Stj7-4fqiDh-fybWgngHxxd97fwP3x-dnEzH5NP38Evb8oHrvzyHsrhZr8wru6V9OfBavw0z4A2EiCCI
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=An+Improved+Chaos+Sparrow+Search+Optimization+Algorithm+Using+Adaptive+Weight+Modification+and+Hybrid+Strategies&rft.jtitle=IEEE+access&rft.au=Zhang%2C+Xuan-Yu&rft.au=Zhou%2C+Kai-Qing&rft.au=Li%2C+Peng-Cheng&rft.au=Xiang%2C+Yin-Hong&rft.date=2022&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=10&rft.spage=96159&rft.epage=96179&rft_id=info:doi/10.1109%2FACCESS.2022.3204798&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_ACCESS_2022_3204798
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