Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Neural computing & applications Ročník 33; číslo 13; s. 7467 - 7490
Hlavní autoři:	Mouassa, Souhil, Jurado, Francisco, Bouktir, Tarek, Raja, Muhammad Asif Zahoor
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.07.2021 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Combinatorial analysis Complexity Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Design optimization Electric power grids Electric power systems Electricity distribution Image Processing and Computer Vision Optimization techniques Original Article Performance evaluation Power dispatch Probability and Statistics in Computer Science Reactive power System effectiveness Variance analysis Voltage stability Real power loss Optimal reactive power dispatch Voltage deviation Voltage stability index Large-scale test system Artificial ecosystem optimization algorithm
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.
AbstractList	Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem. Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.
Author	Jurado, Francisco Raja, Muhammad Asif Zahoor Mouassa, Souhil Bouktir, Tarek
Author_xml	– sequence: 1 givenname: Souhil orcidid: 0000-0001-7812-8184 surname: Mouassa fullname: Mouassa, Souhil email: souhil.mouassa@univ-bouira.dz organization: Department of Electrical Engineering, University of Jaén, Department of Electrical Engineering, University of Bouira, Department of Electrical Engineering, University of Farhat Abbas, Sétif 1 – sequence: 2 givenname: Francisco surname: Jurado fullname: Jurado, Francisco organization: Department of Electrical Engineering, University of Jaén – sequence: 3 givenname: Tarek surname: Bouktir fullname: Bouktir, Tarek organization: Department of Electrical Engineering, University of Bouira – sequence: 4 givenname: Muhammad Asif Zahoor surname: Raja fullname: Raja, Muhammad Asif Zahoor organization: Future Technology Research Center, National Yunlin University of Science and Technology, Department of Electrical and Computer Engineering, COMSATS University Islamabad
BookMark	eNp9UctOHDEQtCKQshB-gJMlzhPar3kc0SovCSWX5Gx5Pe1dr8x4sL2g5TvywTEMUiQOnPrQVV3VVWfkZIoTEnLJ4DMD6K4zgOKsAQ4NKDm0DXwgKyaFaASo_oSsYJB13UrxkZzlvAcA2fZqRf7-jA8Y6IjZbycaHTWpeOetN4GijfmYC97ROBd_558wURcTDSZtscnWBFw2FZvQ2OIfkM7xscJGn2dT7I7OKW5CvfDoy46aeQ7emuLjREukN2GLyZuJYkBbUhUtR7pNfvxETp0JGS9e5zn58_XL7_X35vbXtx_rm9vGCjaUZsNw7IQ0wnFQholxQGcUoJLdKPlgaxpcSGnlxoETLVjOuFJMmHbT9aYbxTm5Wu5Wl_cHzEXv4yFNVVJzJWtAPYi-ovoFZVPMOaHT1ejLEyUZHzQD_dyBXjrQVVW_dKChUvkb6pxqXOn4PkkspFzBU43ov6t3WP8AiRaecw
CitedBy_id	crossref_primary_10_1007_s00521_021_06318_7 crossref_primary_10_1080_17455030_2023_2178827 crossref_primary_10_1007_s11042_023_16919_0 crossref_primary_10_1049_gtd2_13121 crossref_primary_10_1007_s13369_024_08841_w crossref_primary_10_1007_s00202_024_02789_8 crossref_primary_10_1080_01430750_2024_2304721 crossref_primary_10_1007_s12530_022_09425_5 crossref_primary_10_1016_j_egyr_2023_03_036 crossref_primary_10_1109_ACCESS_2022_3152557 crossref_primary_10_3233_JCM_247299 crossref_primary_10_1371_journal_pone_0289129 crossref_primary_10_1109_ACCESS_2023_3328958 crossref_primary_10_1016_j_meadig_2025_100004 crossref_primary_10_1007_s11227_022_04367_w crossref_primary_10_1016_j_compeleceng_2025_110587 crossref_primary_10_1038_s41598_025_02636_1 crossref_primary_10_3390_math11051236
Cites_doi	10.1155/2020/6382507 10.1007/s00521-019-04194-w 10.1155/2019/4670820 10.1016/j.asoc.2016.11.008 10.1016/j.engappai.2017.10.019 10.1016/j.aej.2020.04.051 10.1016/j.ijepes.2013.04.011 10.1016/j.asoc.2018.07.023 10.1016/j.egyr.2020.07.030 10.1016/j.asoc.2017.01.012 10.1007/s00202-019-00895-6 10.1007/s00521-019-04197-7 10.1109/TPWRD.1986.4308013 10.1109/ACCESS.2020.2985763 10.1140/epjp/s13360-019-00059-2 10.1016/j.enconman.2016.12.032 10.1007/s00521-016-2400-y 10.1049/iet-gtd.2011.0681 10.1109/iesm.2015.7380228 10.1016/j.epsr.2010.10.005 10.1016/j.ijepes.2013.08.010 10.1109/MPER.1985.5526580 10.1145/2911451.2911548 10.1109/TPWRS.2010.2051168 10.1145/2939672.2939832 10.24963/ijcai.2019/405 10.1016/j.asoc.2017.10.049 10.1016/j.ijepes.2018.01.024 10.1186/s13662-017-1461-2 10.1016/j.asoc.2017.04.048 10.1016/j.epsr.2019.106031 10.1016/j.asoc.2013.05.005 10.1016/j.ijepes.2014.07.060 10.1016/j.asoc.2020.106252 10.1080/15325008.2015.1112449 10.1016/j.asoc.2018.01.009 10.1016/j.engappai.2014.12.001 10.1109/ISAP.2015.7325566 10.1016/j.asoc.2017.05.057 10.1016/j.matcom.2020.01.005 10.1145/3392717.3392748 10.1016/j.apenergy.2020.115098 10.1016/j.anucene.2019.106982 10.1016/j.asoc.2015.03.041 10.1109/TLA.2016.7530421 10.1016/j.ijepes.2014.10.041
ContentType	Journal Article
Copyright	Springer-Verlag London Ltd., part of Springer Nature 2020 Springer-Verlag London Ltd., part of Springer Nature 2020.
Copyright_xml	– notice: Springer-Verlag London Ltd., part of Springer Nature 2020 – notice: Springer-Verlag London Ltd., part of Springer Nature 2020.
DBID	AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI
DOI	10.1007/s00521-020-05496-0
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central (New) ProQuest Technology Collection ProQuest One ProQuest Central Korea SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition
DatabaseTitle	CrossRef Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Advanced Technologies & Aerospace Collection
Database_xml	– sequence: 1 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1433-3058
EndPage	7490
ExternalDocumentID	10_1007_s00521_020_05496_0
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB DWQXO PKEHL PQEST PQQKQ PQUKI
ID	FETCH-LOGICAL-c319t-b1ed734a3f205a13d9efa50e547d429c0202344c4bf0f360c2125513a6b78a7d3
IEDL.DBID	P5Z
ISICitedReferencesCount	20
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000590219400002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0941-0643
IngestDate	Tue Nov 04 22:03:50 EST 2025 Sat Nov 29 02:59:18 EST 2025 Tue Nov 18 22:08:47 EST 2025 Fri Feb 21 02:48:02 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	13
Keywords	Real power loss Optimal reactive power dispatch Voltage deviation Voltage stability index Large-scale test system Artificial ecosystem optimization algorithm
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-b1ed734a3f205a13d9efa50e547d429c0202344c4bf0f360c2125513a6b78a7d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-7812-8184
PQID	2544688038
PQPubID	2043988
PageCount	24
ParticipantIDs	proquest_journals_2544688038 crossref_citationtrail_10_1007_s00521_020_05496_0 crossref_primary_10_1007_s00521_020_05496_0 springer_journals_10_1007_s00521_020_05496_0
PublicationCentury	2000
PublicationDate	20210700 2021-07-00 20210701
PublicationDateYYYYMMDD	2021-07-01
PublicationDate_xml	– month: 7 year: 2021 text: 20210700
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	Neural computing & applications
PublicationTitleAbbrev	Neural Comput & Applic
PublicationYear	2021
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	El Ela, Abido, Spea (CR24) 2011; 81 Ara, Khan, Razzaq, Hameed, Raja (CR50) 2018; 2018 Attia, El Sehiemy, Hasanien (CR1) 2018; 99 CR39 Shaw, Mukherjee, Ghoshal (CR36) 2014; 55 Ahmad, Faisal, Shoaib, Raja (CR45) 2020; 135 Zhao, Wang (CR26) 2020; 32 Lee, Park, Ortiz (CR31) 1985; 5 Ghasemi, Taghizadeh, Ghavidel, Aghaei, Abbasian (CR8) 2015; 39 Zameer, Muneeb, Mirza, Raja (CR47) 2020; 135 Bhattacharya, Chattopadhyay (CR35) 2010; 4 Ahmad, Raja, Bilal, Ashraf (CR43) 2017; 28 Bukhari, Raja, Sulaiman, Islam, Shoaib, Kumam (CR49) 2020; 8 Saddique, Bhatti, Haroon, Sattar, Amin, Sajjad, ul Haq, Awan, Rasheed (CR17) 2020; 178 Naderi, Narimani, Fathi, Narimani (CR10) 2017; 53 Kessel, Glavitsch (CR25) 1986; 1 Nguyen, Vo, Van Tran, Van Dai (CR6) 2019 Raja, Manzar, Shah, Shah (CR41) 2018; 62 Ng, Mei, Herwan, Mustaffa, Daniyal (CR14) 2017; 59 Zameer, Arshad, Khan, Raja (CR52) 2017; 134 Hinojosa, Araya (CR4) 2013; 13 CR40 El-Sattar, Kamel, El Sehiemy, Jurado, Yu (CR33) 2019; 31 Sabir, Wahab, Umar, Sakar, Raja (CR44) 2020; 172 Yalçın, Çam, Taplamacıoğlu (CR38) 2020; 102 Rajan, Malakar (CR30) 2015; 66 CR19 Mandal, Roy (CR34) 2013; 53 Polprasert, Ongsakul, Dieu (CR37) 2016; 44 Sulaiman, Mustaffa, Mohamed, Aliman (CR15) 2015; 32 CR13 Bukhari, Sulaiman, Raja, Islam, Shoaib, Kumam (CR42) 2020; 59 Mouassa, Bouktir, Salhi (CR11) 2017; 20 Duong, Duong, Phan, Nguyen, Niccolai (CR5) 2020 Amrane, Boudour, Belazzoug (CR29) 2015; 64 Heidari, Abbaspour, Jordehi (CR9) 2017; 57 Sabir, Manzar, Raja, Sheraz, Wazwaz (CR46) 2018; 65 Muhammad, Khan, Asif, Raja, Ullah (CR18) 2020; 6 Mehmood, Zameer, Aslam, Raja (CR48) 2020; 32 CR28 Biswas, Suganthan, Mallipeddi, Amaratunga (CR3) 2018; 68 Chen, Liu, Zhang, Huang (CR7) 2017; 50 Warid (CR2) 2020; 91 CR23 CR22 CR21 CR20 Raja, Mehmood, ur Rehman, Khan, Zameer (CR53) 2018; 71 Duman, Sönmez, Güvenç, Yörükeren (CR32) 2012; 6 Mouassa, Bouktir (CR12) 2018; 38 Gutiérrez, López, Villa (CR16) 2016; 14 Shahid, Zameer, Mehmood, Raja (CR51) 2020; 269 Zimmerman, Murillo Sánchez, Thomas (CR27) 2011; 26 A Rajan (5496_CR30) 2015; 66 PP Biswas (5496_CR3) 2018; 68 MS Saddique (5496_CR17) 2020; 178 P Kessel (5496_CR25) 1986; 1 5496_CR19 R Ng (5496_CR14) 2017; 59 Y Amrane (5496_CR29) 2015; 64 S Mouassa (5496_CR11) 2017; 20 Warid Warid (5496_CR2) 2020; 91 5496_CR13 VH Hinojosa (5496_CR4) 2013; 13 5496_CR40 MAZ Raja (5496_CR41) 2018; 62 A Bhattacharya (5496_CR35) 2010; 4 W Zhao (5496_CR26) 2020; 32 KY Lee (5496_CR31) 1985; 5 B Shaw (5496_CR36) 2014; 55 Z Sabir (5496_CR44) 2020; 172 TL Duong (5496_CR5) 2020 A Zameer (5496_CR52) 2017; 134 E Yalçın (5496_CR38) 2020; 102 AH Bukhari (5496_CR49) 2020; 8 D Gutiérrez (5496_CR16) 2016; 14 RD Zimmerman (5496_CR27) 2011; 26 B Mandal (5496_CR34) 2013; 53 TT Nguyen (5496_CR6) 2019 A Zameer (5496_CR47) 2020; 135 Y Muhammad (5496_CR18) 2020; 6 I Ahmad (5496_CR43) 2017; 28 A Mehmood (5496_CR48) 2020; 32 E Naderi (5496_CR10) 2017; 53 J Polprasert (5496_CR37) 2016; 44 5496_CR39 Z Sabir (5496_CR46) 2018; 65 A Ara (5496_CR50) 2018; 2018 MAZ Raja (5496_CR53) 2018; 71 SA El-Sattar (5496_CR33) 2019; 31 AH Bukhari (5496_CR42) 2020; 59 AF Attia (5496_CR1) 2018; 99 S Mouassa (5496_CR12) 2018; 38 G Chen (5496_CR7) 2017; 50 MH Sulaiman (5496_CR15) 2015; 32 AAA El Ela (5496_CR24) 2011; 81 AA Heidari (5496_CR9) 2017; 57 S Duman (5496_CR32) 2012; 6 M Ghasemi (5496_CR8) 2015; 39 5496_CR28 F Shahid (5496_CR51) 2020; 269 5496_CR23 5496_CR22 SUI Ahmad (5496_CR45) 2020; 135 5496_CR21 5496_CR20
References_xml	– ident: CR22 – volume: 26 start-page: 12 year: 2011 end-page: 19 ident: CR27 article-title: MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education publication-title: Power Syst IEEE Trans – year: 2020 ident: CR5 article-title: Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm publication-title: J Electr Comput Eng doi: 10.1155/2020/6382507 – volume: 57 start-page: 657 year: 2017 end-page: 671 ident: CR9 article-title: Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems publication-title: Appl Soft Comput J – ident: CR39 – volume: 39 start-page: 100 year: 2015 end-page: 108 ident: CR8 article-title: Solving optimal reactive power dispatch problem using a novel teaching-learning-based optimization algorithm publication-title: Eng Appl Artif Intell – volume: 50 start-page: 58 year: 2017 end-page: 70 ident: CR7 article-title: Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints publication-title: Appl Soft Comput J – volume: 28 start-page: 929 year: 2017 end-page: 944 ident: CR43 article-title: Neural network methods to solve the Lane-Emden type equations arising in thermodynamic studies of the spherical gas cloud model publication-title: Neural Comput Appl – volume: 8 start-page: 71326 year: 2020 end-page: 71338 ident: CR49 article-title: Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting publication-title: IEEE Access – volume: 14 start-page: 2253 year: 2016 end-page: 2263 ident: CR16 article-title: Metaheuristic techniques applied to the optimal reactive power dispatch: a review publication-title: IEEE Lat Am Trans – ident: CR21 – volume: 65 start-page: 152 year: 2018 end-page: 169 ident: CR46 article-title: Neuro-heuristics for nonlinear singular Thomas-Fermi systems publication-title: Appl Soft Comput J – volume: 64 start-page: 551 year: 2015 end-page: 561 ident: CR29 article-title: A new optimal reactive power planning based on differential search algorithm publication-title: Int J Electr Power Energy Syst – volume: 135 start-page: 1 year: 2020 end-page: 29 ident: CR45 article-title: A new heuristic computational solver for nonlinear singular Thomas-Fermi system using evolutionary optimized cubic splines publication-title: Eur Phys J Plus – ident: CR19 – volume: 2018 start-page: 1 year: 2018 end-page: 13 ident: CR50 article-title: Wavelets optimization method for evaluation of fractional partial differential equations: an application to financial modelling publication-title: Adv Differ Equ – volume: 59 start-page: 210 year: 2017 end-page: 222 ident: CR14 article-title: Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique publication-title: Appl Soft Comput J – volume: 31 start-page: 8787 year: 2019 end-page: 8806 ident: CR33 article-title: Single- and multi-objective optimal power flow frameworks using Jaya optimization technique publication-title: Neural Comput Appl doi: 10.1007/s00521-019-04194-w – volume: 6 start-page: 2211 year: 2020 end-page: 2229 ident: CR18 article-title: Solution of optimal reactive power dispatch with FACTS devices: a survey publication-title: Energy Rep – volume: 13 start-page: 3839 year: 2013 end-page: 3852 ident: CR4 article-title: Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems publication-title: Appl Soft Comput – volume: 32 start-page: 1 issue: 4 year: 2020 end-page: 43 ident: CR26 article-title: Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm publication-title: Neural Comput Appl – volume: 44 start-page: 518 year: 2016 end-page: 532 ident: CR37 article-title: Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization publication-title: Electr Power Compon Syst – volume: 68 start-page: 81 year: 2018 end-page: 100 ident: CR3 article-title: Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques publication-title: Eng Appl Artif Intell – volume: 53 start-page: 123 year: 2013 end-page: 134 ident: CR34 article-title: Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization publication-title: Int J Electr Power Energy Syst – volume: 32 start-page: 7121 year: 2020 end-page: 7137 ident: CR48 article-title: Design of nature-inspired heuristic paradigm for systems in nonlinear electrical circuits publication-title: Neural Comput Appl – volume: 32 start-page: 286 year: 2015 end-page: 292 ident: CR15 article-title: Using the gray wolf optimizer for solving optimal reactive power dispatch problem publication-title: Appl Soft Comput J – volume: 269 start-page: 115098 year: 2020 ident: CR51 article-title: A novel wavenets long short term memory paradigm for wind power prediction publication-title: Appl Energy – volume: 91 start-page: 106252 year: 2020 ident: CR2 article-title: Optimal power flow using the AMTPG-Jaya algorithm publication-title: Appl Soft Comput J – volume: 5 start-page: 42 year: 1985 end-page: 43 ident: CR31 article-title: A united approach to optimal real and reactive power dispatch publication-title: IEEE Power Eng Rev PER-5 – year: 2019 ident: CR6 article-title: Optimal dispatch of reactive power using modified stochastic fractal search algorithm publication-title: Complexity doi: 10.1155/2019/4670820 – volume: 99 start-page: 331 year: 2018 end-page: 343 ident: CR1 article-title: Optimal power flow solution in power systems using a novel Sine-Cosine algorithm publication-title: Int J Electr Power Energy Syst – volume: 53 start-page: 441 year: 2017 end-page: 456 ident: CR10 article-title: A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch publication-title: Appl Soft Comput J – volume: 135 start-page: 106982 year: 2020 ident: CR47 article-title: Fractional-order particle swarm based multi-objective PWR core loading pattern optimization publication-title: Ann Nucl Energy – volume: 178 start-page: 106031 year: 2020 ident: CR17 article-title: Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques—status and technological review publication-title: Electr Power Syst Res – volume: 55 start-page: 29 year: 2014 end-page: 40 ident: CR36 article-title: Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm publication-title: Int J Electr Power Energy Syst – volume: 134 start-page: 361 year: 2017 end-page: 372 ident: CR52 article-title: Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks publication-title: Energy Convers Manag – ident: CR40 – ident: CR23 – volume: 6 start-page: 563 year: 2012 end-page: 576 ident: CR32 article-title: Optimal reactive power dispatch using a gravitational search algorithm publication-title: IET Gener Transm Distrib – volume: 71 start-page: 622 year: 2018 end-page: 648 ident: CR53 article-title: Bio-inspired computational heuristics for Sisko fluid flow and heat transfer models publication-title: Appl Soft Comput J – volume: 62 start-page: 359 year: 2018 end-page: 372 ident: CR41 article-title: Intelligent computing for Mathieu’s systems for parameter excitation, vertically driven pendulum and dusty plasma models publication-title: Appl Soft Comput J – volume: 38 start-page: 305 year: 2018 end-page: 322 ident: CR12 article-title: Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem publication-title: COMPEL Int J Comput Math Electr Electron Eng – volume: 102 start-page: 573 year: 2020 end-page: 590 ident: CR38 article-title: A new chaos and global competitive ranking—based symbiotic organisms search algorithm for solving reactive power dispatch problem with discrete and continuous control variable publication-title: Electr Eng – volume: 1 start-page: 346 year: 1986 end-page: 354 ident: CR25 article-title: Estimating the voltage stability of a power system publication-title: IEEE Trans Power Deliv – ident: CR13 – volume: 4 start-page: 568 year: 2010 end-page: 576 ident: CR35 article-title: Solution of optimal reactive power flow using biogeography-based optimization publication-title: Int J Electr Electron Eng – volume: 172 start-page: 1 year: 2020 end-page: 14 ident: CR44 article-title: Novel design of Morlet wavelet neural network for solving second order Lane-Emden equation publication-title: Math Comput Simul – volume: 20 start-page: 885 year: 2017 end-page: 895 ident: CR11 article-title: Ant lion optimizer for solving optimal reactive power dispatch problem in power systems publication-title: Eng Sci Technol Int J – ident: CR28 – ident: CR20 – volume: 66 start-page: 9 year: 2015 end-page: 24 ident: CR30 article-title: Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm publication-title: Int J Electr Power Energy Syst – volume: 59 start-page: 3325 year: 2020 end-page: 3345 ident: CR42 article-title: Design of a hybrid NAR-RBFs neural network for nonlinear dusty plasma system publication-title: Alex Eng J – volume: 81 start-page: 458 year: 2011 end-page: 464 ident: CR24 article-title: Differential evolution algorithm for optimal reactive power dispatch publication-title: Electr Power Syst Res – volume: 20 start-page: 885 year: 2017 ident: 5496_CR11 publication-title: Eng Sci Technol Int J – volume: 50 start-page: 58 year: 2017 ident: 5496_CR7 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2016.11.008 – volume: 68 start-page: 81 year: 2018 ident: 5496_CR3 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2017.10.019 – volume: 59 start-page: 3325 year: 2020 ident: 5496_CR42 publication-title: Alex Eng J doi: 10.1016/j.aej.2020.04.051 – volume: 53 start-page: 123 year: 2013 ident: 5496_CR34 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2013.04.011 – year: 2020 ident: 5496_CR5 publication-title: J Electr Comput Eng doi: 10.1155/2020/6382507 – volume: 71 start-page: 622 year: 2018 ident: 5496_CR53 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2018.07.023 – volume: 6 start-page: 2211 year: 2020 ident: 5496_CR18 publication-title: Energy Rep doi: 10.1016/j.egyr.2020.07.030 – ident: 5496_CR40 – volume: 4 start-page: 568 year: 2010 ident: 5496_CR35 publication-title: Int J Electr Electron Eng – volume: 53 start-page: 441 year: 2017 ident: 5496_CR10 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2017.01.012 – volume: 102 start-page: 573 year: 2020 ident: 5496_CR38 publication-title: Electr Eng doi: 10.1007/s00202-019-00895-6 – volume: 32 start-page: 7121 year: 2020 ident: 5496_CR48 publication-title: Neural Comput Appl doi: 10.1007/s00521-019-04197-7 – volume: 1 start-page: 346 year: 1986 ident: 5496_CR25 publication-title: IEEE Trans Power Deliv doi: 10.1109/TPWRD.1986.4308013 – volume: 8 start-page: 71326 year: 2020 ident: 5496_CR49 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2985763 – volume: 135 start-page: 1 year: 2020 ident: 5496_CR45 publication-title: Eur Phys J Plus doi: 10.1140/epjp/s13360-019-00059-2 – ident: 5496_CR21 – volume: 134 start-page: 361 year: 2017 ident: 5496_CR52 publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.12.032 – volume: 28 start-page: 929 year: 2017 ident: 5496_CR43 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2400-y – volume: 6 start-page: 563 year: 2012 ident: 5496_CR32 publication-title: IET Gener Transm Distrib doi: 10.1049/iet-gtd.2011.0681 – ident: 5496_CR13 doi: 10.1109/iesm.2015.7380228 – volume: 81 start-page: 458 year: 2011 ident: 5496_CR24 publication-title: Electr Power Syst Res doi: 10.1016/j.epsr.2010.10.005 – volume: 55 start-page: 29 year: 2014 ident: 5496_CR36 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2013.08.010 – volume: 5 start-page: 42 year: 1985 ident: 5496_CR31 publication-title: IEEE Power Eng Rev PER-5 doi: 10.1109/MPER.1985.5526580 – volume: 31 start-page: 8787 year: 2019 ident: 5496_CR33 publication-title: Neural Comput Appl doi: 10.1007/s00521-019-04194-w – ident: 5496_CR20 doi: 10.1145/2911451.2911548 – volume: 26 start-page: 12 year: 2011 ident: 5496_CR27 publication-title: Power Syst IEEE Trans doi: 10.1109/TPWRS.2010.2051168 – ident: 5496_CR23 doi: 10.1145/2939672.2939832 – volume: 38 start-page: 305 year: 2018 ident: 5496_CR12 publication-title: COMPEL Int J Comput Math Electr Electron Eng – ident: 5496_CR19 doi: 10.24963/ijcai.2019/405 – volume: 62 start-page: 359 year: 2018 ident: 5496_CR41 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2017.10.049 – volume: 99 start-page: 331 year: 2018 ident: 5496_CR1 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2018.01.024 – volume: 2018 start-page: 1 year: 2018 ident: 5496_CR50 publication-title: Adv Differ Equ doi: 10.1186/s13662-017-1461-2 – volume: 57 start-page: 657 year: 2017 ident: 5496_CR9 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2017.04.048 – volume: 178 start-page: 106031 year: 2020 ident: 5496_CR17 publication-title: Electr Power Syst Res doi: 10.1016/j.epsr.2019.106031 – ident: 5496_CR39 – volume: 13 start-page: 3839 year: 2013 ident: 5496_CR4 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2013.05.005 – volume: 64 start-page: 551 year: 2015 ident: 5496_CR29 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2014.07.060 – volume: 91 start-page: 106252 year: 2020 ident: 5496_CR2 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2020.106252 – volume: 44 start-page: 518 year: 2016 ident: 5496_CR37 publication-title: Electr Power Compon Syst doi: 10.1080/15325008.2015.1112449 – volume: 65 start-page: 152 year: 2018 ident: 5496_CR46 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2018.01.009 – volume: 39 start-page: 100 year: 2015 ident: 5496_CR8 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2014.12.001 – ident: 5496_CR28 doi: 10.1109/ISAP.2015.7325566 – year: 2019 ident: 5496_CR6 publication-title: Complexity doi: 10.1155/2019/4670820 – volume: 32 start-page: 1 issue: 4 year: 2020 ident: 5496_CR26 publication-title: Neural Comput Appl – volume: 59 start-page: 210 year: 2017 ident: 5496_CR14 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2017.05.057 – volume: 172 start-page: 1 year: 2020 ident: 5496_CR44 publication-title: Math Comput Simul doi: 10.1016/j.matcom.2020.01.005 – ident: 5496_CR22 doi: 10.1145/3392717.3392748 – volume: 269 start-page: 115098 year: 2020 ident: 5496_CR51 publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115098 – volume: 135 start-page: 106982 year: 2020 ident: 5496_CR47 publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2019.106982 – volume: 32 start-page: 286 year: 2015 ident: 5496_CR15 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2015.03.041 – volume: 14 start-page: 2253 year: 2016 ident: 5496_CR16 publication-title: IEEE Lat Am Trans doi: 10.1109/TLA.2016.7530421 – volume: 66 start-page: 9 year: 2015 ident: 5496_CR30 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2014.10.041
SSID	ssj0004685
Score	2.3630714
Snippet	Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	7467
SubjectTerms	Algorithms Artificial Intelligence Combinatorial analysis Complexity Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Design optimization Electric power grids Electric power systems Electricity distribution Image Processing and Computer Vision Optimization techniques Original Article Performance evaluation Power dispatch Probability and Statistics in Computer Science Reactive power System effectiveness Variance analysis Voltage stability
SummonAdditionalLinks	– databaseName: Springer LINK dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEF60evBifWK1yhy8aSDvx7GIxYMU8UVvIdmHBGpTmljQ3-EPdmaTtFVU0Gv2FXZmZ2Z3Hh9jp55SkRdxafipKQ2y0Y1E4bnigjupFYogSnSd2etgMAiHw-imTgormmj3xiWpJfU82Y1eMPHqa5Pz1qXQ2VW2huouJMCG27vHpWxIDcSJ9xaK6XGdOlXm-zk-q6OFjfnFLaq1Tb_9v__cYpu1dQm9ih222Yoc77B2g9wA9UHeZe-DfCZHIHT4BuQKiIGqWhKA99GqvDPkKE6eszcciJYtjChm3CiQprJqwb5ocWp5CRMCWwORoXxCNoAapgbolReWfORQ5tAbPRHXj6FC4MFFy1d4mmZijz30L-8vrowan8HgeHBLI7WkCBw3cZRteonliEiqxDOl5wYC1Rw3CZrddbmbKlM5vslRTRKeTOKnQZgEwtlnrXE-lgcMlK_s1CYnK94HfeVHaRgEpi0EfrQSKTrMasgU87p4OWFojOJ52WW97TGuGettj80OO5uPmVSlO37t3W2oH9fHuIipfpuPEs4JO-y8ofai-efZDv_W_Yht2BQro8OAu6xVTl_kMVvnszIrpieavT8AxiT2Aw priority: 102 providerName: Springer Nature
Title	Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid
URI	https://link.springer.com/article/10.1007/s00521-020-05496-0 https://www.proquest.com/docview/2544688038
Volume	33
WOSCitedRecordID	wos000590219400002&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: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: Springer LINK customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9tAEB6VhAOXpuUhAhTNoTdq4ffjhAAF9VBFEdAK9WLZ-0CRQpwmJhL8Dn4wM-s1oUjl0osP-7Q0j52dmZ0P4GukdRZlQjlx6SqHbXSn0CRXQoqg9FKZZIWpM_sjGQ7Tm5tsZB1uC5tW2epEo6hlJdhHfsyltGJitiA9mf1xGDWKo6sWQmMNulwlgaEbRtHvV-8iDSQn3WA4uycM7KMZ83SO_aHU6nMoOORE3L8PppW1-SZAas6di97__vEn-GgtTjxtWOQzfFDTTei1aA5ohXsLnobVUk1QmpQOrDQyUzX1JZDuqE3JZ6xIxdyNH2kiWbs44TxyZ0F0Vk0PjSUr1OhQnDEAG8ox6SxiDbTQNcieX3wVN8e6wtPJLUvCFBtUHtq0fsDb-Vhuw8-LwfX5d8diNjiChLl2Sk_JJAiLQPtuVHiBzJQuIldFYSLp6BMuw7WHoQhL7eogdgUdnYwxU8RlkhaJDHagM62mahdQx9ovfQ680h0x1nFWpkni-lJSo1co2QevJVgubEFzxtWY5C-lmA2Rc9ozN0TO3T4cvcyZNeU83h190FI2t6K9yFdk7cO3ljdW3f9ebe_91fZhw-d8GZMKfACden6vvsC6WNbjxfwQumeD4ejy0DA4fS-vfj0DEvsDEQ
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgkQvlKe6UGAOcIIIx3FeB4QqoGrVZdVDkSouIfGjWmnZbHfTVu3v4HfwG5lxkm5BorceuNqxLTnfjMeexwfwKnYuj3Ntg6QSNmAbPSgdyZU2OqrCzKR56evMDtPRKDs8zPdX4FefC8Nhlb1O9Ira1JrfyN9xKa2EwBZlH2bHAbNGsXe1p9BoYbFnz8_oyrZ4v_uJ_u9rKbc_H3zcCTpWgUAT3JqgCq1JI1VGToq4DCOTW1fGwsYqNaSctWBCcaW0qpxwUSI0KXdmQSmTKs3K1EQ07y24rZQULEX78bcreZieApRuTBxNpKIuScen6vH7K7VKdj0rDvz98yBcWrd_OWT9Obe9_r_t0H2411nUuNWKwANYsdOHsN6zVWCnvB7Bz1F9aidofMgK1g5ZaNr6GUh38LakNdakQn-ML2ggWfM44Tj5YEE4tm0PfUtWtj8jcMYEc2jGpJMJ-thR8yC_bOOVuABsatyaHLGkT7FlHaJFm3M8mo_NY_h6I3vzBFan9dRuALrEyUqyY5nuwIlL8ipLUyGNocawtGYAYQ-QQncF25k3ZFJclpr2oCpozcKDqhADeHM5ZtaWK7n2680eSUWnuhbFEkYDeNtjcdn979meXj_bS7i7c_BlWAx3R3vPYE1ybJAPe96E1WZ-Yp_DHX3ajBfzF16oEL7fNEZ_AzUXW3I
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9NAEB5BQKgvBAqIQArzwFux6vt4jIAIRBRF4lDeLHuPyFJqR4kbqf0d_cGdWdtJWhUkxKv3snZnZmd2jg_gQ6B1EiRCWWFuK4t1dCvTxFdCCi93YhklmakzO4mm03g-T2YHWfwm2r1zSTY5DVylqazPVlKf7RLf-DWTzGCXHbk-h9E-hEc-B9Kzvf7j90FmpAHlJBuG43t8r02buX-O21fTXt-84yI1N8-4_____AyetlonjhoyeQ4PVHkM_Q7RAVsGfwHX02qrlihNWAdWGpmwmhoTSHZqU_YZKxIz58UVDSSNF5ccS25t6KxV00J9SRM1chRXDMKGsiC5ReSBLXwN8usvHvjOsa5wtFwwN5TYIPPQovUlLtaFfAm_xl9-fvpqtbgNliCGrq3cUTLy_MzTrh1kjicTpbPAVoEfSbr-hM2Q7b4v_Fzb2gttQdcn48xkYR7FWSS9V9Arq1K9BtShdnOXna9kJ4Y6TPI4imxXSvroZEoOwOmOLBVtUXPG1limu3LMZttTWjM1257aAzjdjVk1JT3-2nvYUULasvcm5bpuIUk-Lx7Ax-7k981_nu3Nv3V_D09mn8fp5Nv0-1s4cjmcxkQKD6FXry_UCTwW27rYrN8Zqr8BJXkB2g
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=Novel+design+of+artificial+ecosystem+optimizer+for+large-scale+optimal+reactive+power+dispatch+problem+with+application+to+Algerian+electricity+grid&rft.jtitle=Neural+computing+%26+applications&rft.au=Mouassa%2C+Souhil&rft.au=Jurado%2C+Francisco&rft.au=Bouktir%2C+Tarek&rft.au=Raja%2C+Muhammad+Asif+Zahoor&rft.date=2021-07-01&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=33&rft.issue=13&rft.spage=7467&rft.epage=7490&rft_id=info:doi/10.1007%2Fs00521-020-05496-0&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s00521_020_05496_0
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon