Electric fish optimization: a new heuristic algorithm inspired by electrolocation

Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Neural computing & applications Jg. 32; H. 15; S. 11543 - 11578
Hauptverfasser:	Yilmaz, Selim, Sen, Sevil
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.08.2020 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Clustering Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer simulation Data Mining and Knowledge Discovery Evolutionary algorithms Evolutionary computation Fish Functions (mathematics) Genetic algorithms Heuristic Heuristic methods Image Processing and Computer Vision Mathematical functions Optimization Original Article Particle swarm optimization Probability and Statistics in Computer Science Simulated annealing Swarm intelligence Real-world applications Heuristics Single-solution algorithms Swarm intelligence Nature-inspired algorithm Real parameter optimization Population-based algorithms
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in this study inspired by the prey location and communication behaviors of electric fish. Nocturnal electric fish have very poor eyesight and live in muddy, murky water, where visual senses are very limited. Therefore, they rely on their species-specific ability called electrolocation to perceive their environment. The active and passive electrolocation capability of such fish is believed to be a good candidate for balancing local and global search, and hence it is modeled in this study. A new heuristic called electric fish optimization (EFO) is introduced and compared with six well-known heuristics (simulated annealing, SA; vortex search, VS; genetic algorithm, GA; differential evolution, DE; particle swarm optimization, PSO; and artificial bee colony, ABC). In the experiments, 50 basic and 30 complex mathematical functions, 13 clustering problems, and five real-world design problems are used as the benchmark sets. The simulation results indicate that EFO is better than or very competitive with its competitors.
AbstractList	Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in this study inspired by the prey location and communication behaviors of electric fish. Nocturnal electric fish have very poor eyesight and live in muddy, murky water, where visual senses are very limited. Therefore, they rely on their species-specific ability called electrolocation to perceive their environment. The active and passive electrolocation capability of such fish is believed to be a good candidate for balancing local and global search, and hence it is modeled in this study. A new heuristic called electric fish optimization (EFO) is introduced and compared with six well-known heuristics (simulated annealing, SA; vortex search, VS; genetic algorithm, GA; differential evolution, DE; particle swarm optimization, PSO; and artificial bee colony, ABC). In the experiments, 50 basic and 30 complex mathematical functions, 13 clustering problems, and five real-world design problems are used as the benchmark sets. The simulation results indicate that EFO is better than or very competitive with its competitors.
Author	Sen, Sevil Yilmaz, Selim
Author_xml	– sequence: 1 givenname: Selim orcidid: 0000-0002-9516-6892 surname: Yilmaz fullname: Yilmaz, Selim email: selimy@cs.hacettepe.edu.tr organization: WISE Lab, Hacettepe University – sequence: 2 givenname: Sevil orcidid: 0000-0001-5814-9973 surname: Sen fullname: Sen, Sevil organization: WISE Lab, Hacettepe University
BookMark	eNp9kMtKAzEUhoMo2FZfwNWA69GT20zGnZR6gYIIug5p5kybMp3UJEXq0zt2BMFFVwcO33cu_5icdr5DQq4o3FCA8jYCSEZzoFUOohA0VydkRAXnOQepTskIqr4JheDnZBzjGqDHlByR11mLNgVns8bFVea3yW3cl0nOd3eZyTr8zFa4Cy6mHjHt0geXVpvMdXHrAtbZYp_hYYJvvT1oF-SsMW3Ey986Ie8Ps7fpUz5_eXye3s9zKwBSXjOBUgBDKhAXhmFRGL7AUlYoKiWrWlha1ihZg6YWZW0RmsYWUCGzSgHwCbke5m6D_9hhTHrtd6HrV2rOqJKipKo8RjHBhCw447yn1EDZ4GMM2Gjr0uGbFIxrNQX9E7MeYtZ9zPoQs1a9yv6p2-A2JuyPS3yQYg93Swx_Vx2xvgH_35IL
CitedBy_id	crossref_primary_10_1002_cpe_7388 crossref_primary_10_1007_s12065_023_00834_2 crossref_primary_10_1002_cpe_7547 crossref_primary_10_1109_ACCESS_2020_3036990 crossref_primary_10_1016_j_asoc_2025_113872 crossref_primary_10_3233_JIFS_221262 crossref_primary_10_1007_s12083_022_01336_1 crossref_primary_10_3390_e23091189 crossref_primary_10_3390_infrastructures10090252 crossref_primary_10_1142_S0219649222500423 crossref_primary_10_1088_1402_4896_ade378 crossref_primary_10_1016_j_eswa_2025_129670 crossref_primary_10_1002_ett_4937 crossref_primary_10_1007_s11042_021_11469_9 crossref_primary_10_1016_j_eswa_2023_122200 crossref_primary_10_1016_j_foodchem_2025_144783 crossref_primary_10_1155_2022_4875399 crossref_primary_10_1016_j_cma_2022_114901 crossref_primary_10_3390_math10234565 crossref_primary_10_1007_s00521_024_09889_3 crossref_primary_10_1007_s12083_025_02065_x crossref_primary_10_1007_s00500_023_07930_6 crossref_primary_10_3390_drones7070427 crossref_primary_10_3390_electronics11213463 crossref_primary_10_1007_s11760_024_03617_z crossref_primary_10_1007_s12065_024_00955_2 crossref_primary_10_1007_s12065_022_00742_x crossref_primary_10_1080_0954898X_2024_2349275 crossref_primary_10_3389_fenrg_2022_1011887 crossref_primary_10_1016_j_jnca_2022_103385 crossref_primary_10_1080_10255842_2023_2187662 crossref_primary_10_1007_s10115_023_01865_y crossref_primary_10_1007_s11277_023_10781_x crossref_primary_10_1002_ett_4640 crossref_primary_10_1007_s11227_022_04886_6 crossref_primary_10_1080_01969722_2022_2145661 crossref_primary_10_1016_j_bspc_2023_104971 crossref_primary_10_1142_S0219649222500642 crossref_primary_10_1007_s11042_023_17351_0 crossref_primary_10_1080_17508975_2025_2478061 crossref_primary_10_3390_s23052445 crossref_primary_10_1007_s11276_022_03022_9 crossref_primary_10_1007_s13042_024_02197_1 crossref_primary_10_3390_math11041032 crossref_primary_10_1109_ACCESS_2025_3527031 crossref_primary_10_1016_j_bspc_2023_104809 crossref_primary_10_3233_IDT_220201 crossref_primary_10_1007_s40998_023_00664_z crossref_primary_10_1007_s12652_022_03783_3 crossref_primary_10_1016_j_engappai_2022_105521 crossref_primary_10_1007_s00366_021_01431_6 crossref_primary_10_1007_s12083_024_01666_2 crossref_primary_10_1016_j_engappai_2023_106112 crossref_primary_10_3390_biomimetics9110677 crossref_primary_10_1016_j_eswa_2021_115436 crossref_primary_10_1016_j_asoc_2024_112271 crossref_primary_10_1007_s11277_021_08544_7 crossref_primary_10_1007_s11042_022_12874_4 crossref_primary_10_1109_ACCESS_2022_3152160 crossref_primary_10_1007_s12046_023_02092_5 crossref_primary_10_1080_01969722_2022_2151173 crossref_primary_10_3233_IDT_210182 crossref_primary_10_1007_s11760_025_04501_0 crossref_primary_10_3390_app13158960 crossref_primary_10_1016_j_bspc_2021_102935 crossref_primary_10_1038_s41598_022_15170_1 crossref_primary_10_1109_ACCESS_2024_3476157 crossref_primary_10_1016_j_eswa_2023_122873 crossref_primary_10_1002_ett_4723 crossref_primary_10_1007_s11831_022_09711_0 crossref_primary_10_3389_fenrg_2022_946864 crossref_primary_10_1080_10106049_2022_2138567
Cites_doi	10.1007/s10898-007-9149-x 10.1007/s00521-017-3329-5 10.1109/joe.2004.833210 10.1016/j.amc.2009.03.090 10.1007/s10462-009-9127-4 10.1002/9780470496916 10.1016/j.media.2017.07.005 10.1016/j.cad.2010.12.015 10.1016/S0031-3203(01)00108-X 10.1093/oso/9780195131581.001.0001 10.1002/ett.1062 10.1023/a:1012443124333 10.1007/BF02430363 10.1109/ICNN.1995.488968 10.1016/0022-2569(70)90064-9 10.1002/nme.2904 10.1016/j.comnet.2011.07.001 10.1242/jeb.082743 10.1145/1276958.1276994 10.1016/j.ins.2009.03.004 10.1109/iros.2010.5648929 10.1016/j.neucom.2019.02.056 10.1016/j.asoc.2005.09.004 10.1007/s10845-010-0393-4 10.1016/j.asoc.2009.08.031 10.1109/MCS.2002.1004010 10.1126/science.220.4598.671 10.1109/ROBOT.2007.364231 10.1016/j.asoc.2016.08.056 10.1016/j.ejor.2008.07.025 10.1007/s00500-016-2389-2 10.1504/IJBIC.2010.032124 10.1016/j.amc.2013.02.017 10.1109/CEC.2007.4424532 10.1016/j.compstruc.2016.03.001 10.1145/2576768.2598262 10.1016/j.eswa.2009.06.044 10.1016/j.isatra.2014.03.018 10.1049/el:19980096 10.1016/j.compag.2018.02.016 10.1109/CEC.2008.4630985 10.1016/j.ins.2013.02.041 10.1007/s00500-016-2033-1 10.1007/s12555-016-0338-6 10.1007/s00500-005-0511-y 10.1115/1.2912596 10.1016/j.inffus.2017.10.006 10.1109/TAI.2003.1250183 10.1016/j.parco.2003.12.015 10.1109/NABIC.2009.5393690 10.1080/0305215X.2013.836640 10.1016/j.ins.2012.08.023 10.1016/j.asoc.2007.10.013 10.1007/s10845-017-1294-6 10.1016/j.asoc.2009.12.025 10.1016/j.asoc.2011.01.034 10.1016/j.swevo.2011.02.002 10.1016/j.neucom.2017.09.065 10.1016/j.apm.2017.08.013 10.7551/mitpress/1090.001.0001 10.1007/s12293-016-0198-x 10.1016/j.knosys.2011.07.001 10.1016/j.ins.2008.02.014 10.1007/978-3-662-05094-1 10.1109/4235.585893 10.1016/j.amc.2009.01.048
ContentType	Journal Article
Copyright	Springer-Verlag London Ltd., part of Springer Nature 2019 Springer-Verlag London Ltd., part of Springer Nature 2019. Copyright Springer Nature B.V. Aug 2020
Copyright_xml	– notice: Springer-Verlag London Ltd., part of Springer Nature 2019 – notice: Springer-Verlag London Ltd., part of Springer Nature 2019. – notice: Copyright Springer Nature B.V. Aug 2020
DBID	AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS
DOI	10.1007/s00521-019-04641-8
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Technology Collection ProQuest One Community College 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 ProQuest Central China
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 China 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 Advanced Technologies & Aerospace Collection
Database_xml	– sequence: 1 dbid: P5Z name: ProQuest advanced technologies & aerospace journals url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1433-3058
EndPage	11578
ExternalDocumentID	10_1007_s00521_019_04641_8
GrantInformation_xml	– fundername: Türkiye Bilimsel ve Teknolojik Arastirma Kurumu grantid: 2228 funderid: http://dx.doi.org/10.13039/501100004410
GroupedDBID	-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 AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBRH ABBXA ABDBE ABDBF ABDZT ABECU ABFSG ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACSTC ACUHS ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHWEU AHYZX AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA 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 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 PHGZM PHGZT PQGLB 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 ZMTXR ~8M ~EX AAYXX AFFHD CITATION DWQXO PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c400t-d24e5402e14eeba2e66a3be759e49859d4c17de52fead47dce0ffc609e2c88003
IEDL.DBID	RSV
ISICitedReferencesCount	94
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000500866600003&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	Wed Nov 05 09:30:56 EST 2025 Tue Nov 04 23:02:56 EST 2025 Sat Nov 29 07:45:37 EST 2025 Tue Nov 18 22:08:47 EST 2025 Mon Jul 21 06:07:21 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	15
Keywords	Real-world applications Heuristics Single-solution algorithms Swarm intelligence Nature-inspired algorithm Real parameter optimization Population-based algorithms
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c400t-d24e5402e14eeba2e66a3be759e49859d4c17de52fead47dce0ffc609e2c88003
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-5814-9973 0000-0002-9516-6892
PQID	2424563233
PQPubID	2043988
PageCount	36
ParticipantIDs	proquest_journals_3218547187 proquest_journals_2424563233 crossref_citationtrail_10_1007_s00521_019_04641_8 crossref_primary_10_1007_s00521_019_04641_8 springer_journals_10_1007_s00521_019_04641_8
PublicationCentury	2000
PublicationDate	2020-08-01
PublicationDateYYYYMMDD	2020-08-01
PublicationDate_xml	– month: 08 year: 2020 text: 2020-08-01 day: 01
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	Neural computing & applications
PublicationTitleAbbrev	Neural Comput & Applic
PublicationYear	2020
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	D Karaboga (4641_CR35) 2009; 214 4641_CR8 M Zhang (4641_CR83) 2008; 178 4641_CR9 G Di Caro (4641_CR18) 2005; 16 D Karaboga (4641_CR36) 2009; 31 4641_CR61 XS Yang (4641_CR80) 2013 4641_CR20 4641_CR64 4641_CR5 D Datta (4641_CR16) 2011; 11 4641_CR66 AH Gandomi (4641_CR25) 2011 SS Rao (4641_CR59) 1996 4641_CR65 RS Barr (4641_CR7) 1995; 1 4641_CR26 4641_CR27 P Moller (4641_CR52) 1995 B Akay (4641_CR1) 2012; 23 K Tan (4641_CR70) 2009; 197 J Derrac (4641_CR17) 2011; 1 G Kanagaraj (4641_CR33) 2014; 46 ID Falco (4641_CR22) 2008; 8 P Civicioglu (4641_CR13) 2013; 219 D Karaboga (4641_CR37) 2007; 39 M MacIver (4641_CR45) 2004; 29 JS Arora (4641_CR3) 1967 4641_CR50 E Rashedi (4641_CR60) 2009; 179 S Opricovic (4641_CR55) 1998; 2 4641_CR58 P Civicioglu (4641_CR14) 2013; 219 AH Gandomi (4641_CR24) 2014; 53 CL dos Santos (4641_CR63) 2010; 37 V Aragon (4641_CR2) 2010; 84 H Nezamabadi-pour (4641_CR54) 2006; 10 A Askarzadeh (4641_CR4) 2016; 169 MA Maciver (4641_CR46) 2001; 11 I Boussaïd (4641_CR12) 2013; 237 N Zhang (4641_CR84) 2017; 9 D Karaboga (4641_CR38) 2011; 11 4641_CR81 4641_CR40 XS Yang (4641_CR77) 2010; 2 4641_CR42 4641_CR44 4641_CR43 4641_CR87 4641_CR48 (4641_CR15) 1999 4641_CR47 4641_CR49 S Bandyopadhyay (4641_CR6) 2002; 35 S Fan (4641_CR23) 2018; 22 CL Hwang (4641_CR31) 2012 XS Yang (4641_CR78) 2010 ID Falco (4641_CR21) 2007; 7 KM Passino (4641_CR57) 2002; 22 X Yang (4641_CR76) 2012 J Zhang (4641_CR82) 2009; 211 J Han (4641_CR28) 2018; 16 B Kramer (4641_CR41) 1996 EG Talbi (4641_CR69) 2009 N Zhang (4641_CR85) 2017; 52 4641_CR71 XS Yang (4641_CR79) 2013 A Hatamlou (4641_CR29) 2013; 222 4641_CR73 E Bonabeau (4641_CR11) 1999 T Sousa (4641_CR67) 2004; 30 4641_CR72 4641_CR75 4641_CR32 4641_CR34 TS Du (4641_CR19) 2018; 55 WT Pan (4641_CR56) 2012; 26 4641_CR39 ID Neveln (4641_CR53) 2013; 216 N Zhang (4641_CR86) 2018; 275 JH Holland (4641_CR30) 1992 AW Mohamed (4641_CR51) 2018; 29 E Sandgren (4641_CR62) 1990; 112 C Blake (4641_CR10) 1998 F Sun (4641_CR68) 1998; 34 DH Wolpert (4641_CR74) 1997; 1
References_xml	– volume: 39 start-page: 459 issue: 3 year: 2007 ident: 4641_CR37 publication-title: J Global Optim doi: 10.1007/s10898-007-9149-x – ident: 4641_CR71 doi: 10.1007/s00521-017-3329-5 – volume: 29 start-page: 651 issue: 3 year: 2004 ident: 4641_CR45 publication-title: IEEE J Oceanic Eng doi: 10.1109/joe.2004.833210 – volume: 214 start-page: 108 issue: 1 year: 2009 ident: 4641_CR35 publication-title: Appl Math Comput doi: 10.1016/j.amc.2009.03.090 – volume: 31 start-page: 61 issue: 1 year: 2009 ident: 4641_CR36 publication-title: Artif Intell Rev doi: 10.1007/s10462-009-9127-4 – ident: 4641_CR64 – volume-title: Metaheuristics in water, geotechnical and transport engineering year: 2012 ident: 4641_CR76 – volume-title: Metaheuristics: from design to implementation year: 2009 ident: 4641_CR69 doi: 10.1002/9780470496916 – ident: 4641_CR43 doi: 10.1016/j.media.2017.07.005 – ident: 4641_CR58 doi: 10.1016/j.cad.2010.12.015 – volume: 35 start-page: 1197 issue: 6 year: 2002 ident: 4641_CR6 publication-title: Pattern Recogn doi: 10.1016/S0031-3203(01)00108-X – volume-title: Swarm intelligence: from natural to artificial systems year: 1999 ident: 4641_CR11 doi: 10.1093/oso/9780195131581.001.0001 – volume: 16 start-page: 443 issue: 5 year: 2005 ident: 4641_CR18 publication-title: Eur Trans Telecommun doi: 10.1002/ett.1062 – volume-title: Electric fishes: history and behavior. Chapman and Hall fish and fisheries series year: 1995 ident: 4641_CR52 – volume-title: Swarm intelligence and bio-inspired computation: theory and applications year: 2013 ident: 4641_CR80 – volume: 11 start-page: 263 issue: 3 year: 2001 ident: 4641_CR46 publication-title: Auton Robot doi: 10.1023/a:1012443124333 – volume: 1 start-page: 9 issue: 1 year: 1995 ident: 4641_CR7 publication-title: J Heuristics doi: 10.1007/BF02430363 – ident: 4641_CR5 – ident: 4641_CR39 doi: 10.1109/ICNN.1995.488968 – ident: 4641_CR26 doi: 10.1016/0022-2569(70)90064-9 – start-page: 1 volume-title: Metaheuristics in water, geotechnical and transport engineering year: 2013 ident: 4641_CR79 – volume: 84 start-page: 351 issue: 3 year: 2010 ident: 4641_CR2 publication-title: Int J Numer Meth Eng doi: 10.1002/nme.2904 – ident: 4641_CR65 doi: 10.1016/j.comnet.2011.07.001 – ident: 4641_CR34 – volume: 216 start-page: 2501 issue: 13 year: 2013 ident: 4641_CR53 publication-title: J Exp Biol doi: 10.1242/jeb.082743 – ident: 4641_CR61 doi: 10.1145/1276958.1276994 – volume: 179 start-page: 2232 issue: 13 year: 2009 ident: 4641_CR60 publication-title: Inf Sci doi: 10.1016/j.ins.2009.03.004 – ident: 4641_CR42 doi: 10.1109/iros.2010.5648929 – ident: 4641_CR47 doi: 10.1016/j.neucom.2019.02.056 – volume: 7 start-page: 652 issue: 3 year: 2007 ident: 4641_CR21 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2005.09.004 – volume: 23 start-page: 1001 issue: 4 year: 2012 ident: 4641_CR1 publication-title: J Intell Manuf doi: 10.1007/s10845-010-0393-4 – ident: 4641_CR44 doi: 10.1016/j.asoc.2009.08.031 – volume-title: Introduction to optimum design, 1989 year: 1967 ident: 4641_CR3 – volume: 22 start-page: 52 issue: 3 year: 2002 ident: 4641_CR57 publication-title: IEEE Control Syst Mag doi: 10.1109/MCS.2002.1004010 – ident: 4641_CR40 doi: 10.1126/science.220.4598.671 – ident: 4641_CR66 doi: 10.1109/ROBOT.2007.364231 – volume: 52 start-page: 1210 year: 2017 ident: 4641_CR85 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2016.08.056 – ident: 4641_CR48 – volume-title: New ideas in optimization year: 1999 ident: 4641_CR15 – volume: 197 start-page: 701 issue: 2 year: 2009 ident: 4641_CR70 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2008.07.025 – volume: 22 start-page: 861 issue: 3 year: 2018 ident: 4641_CR23 publication-title: Soft Comput doi: 10.1007/s00500-016-2389-2 – ident: 4641_CR50 – volume-title: University of california at Irvine repository of machine learning databases year: 1998 ident: 4641_CR10 – volume: 2 start-page: 78 issue: 2 year: 2010 ident: 4641_CR77 publication-title: Int J Bio-Inspir Comput doi: 10.1504/IJBIC.2010.032124 – volume: 219 start-page: 8121 issue: 15 year: 2013 ident: 4641_CR14 publication-title: Appl Math Comput doi: 10.1016/j.amc.2013.02.017 – ident: 4641_CR8 doi: 10.1109/CEC.2007.4424532 – volume-title: Engineering optimization: theory and practice year: 1996 ident: 4641_CR59 – volume-title: Electroreception and communication in fishes year: 1996 ident: 4641_CR41 – volume: 169 start-page: 1 year: 2016 ident: 4641_CR4 publication-title: Comput Struct doi: 10.1016/j.compstruc.2016.03.001 – ident: 4641_CR81 doi: 10.1145/2576768.2598262 – volume: 37 start-page: 1676 issue: 2 year: 2010 ident: 4641_CR63 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2009.06.044 – volume: 53 start-page: 1168 issue: 4 year: 2014 ident: 4641_CR24 publication-title: ISA Trans doi: 10.1016/j.isatra.2014.03.018 – volume: 34 start-page: 1563 issue: 16 year: 1998 ident: 4641_CR68 publication-title: Electron Lett doi: 10.1049/el:19980096 – ident: 4641_CR32 doi: 10.1016/j.compag.2018.02.016 – ident: 4641_CR9 doi: 10.1109/CEC.2008.4630985 – volume: 237 start-page: 82 year: 2013 ident: 4641_CR12 publication-title: Inf Sci doi: 10.1016/j.ins.2013.02.041 – ident: 4641_CR27 doi: 10.1007/s00500-016-2033-1 – ident: 4641_CR72 – start-page: 259 volume-title: Benchmark problems in structural optimization year: 2011 ident: 4641_CR25 – volume: 16 start-page: 522 issue: 2 year: 2018 ident: 4641_CR28 publication-title: Int J Control Autom Syst doi: 10.1007/s12555-016-0338-6 – volume: 10 start-page: 623 issue: 7 year: 2006 ident: 4641_CR54 publication-title: Soft Comput doi: 10.1007/s00500-005-0511-y – volume: 112 start-page: 223 issue: 2 year: 1990 ident: 4641_CR62 publication-title: J Mech Des doi: 10.1115/1.2912596 – volume: 219 start-page: 8121 issue: 15 year: 2013 ident: 4641_CR13 publication-title: Appl Math Comput doi: 10.1016/j.amc.2013.02.017 – ident: 4641_CR87 doi: 10.1016/j.inffus.2017.10.006 – volume-title: Multiple attribute decision making: methods and applications a state-of-the-art survey year: 2012 ident: 4641_CR31 – ident: 4641_CR49 doi: 10.1109/TAI.2003.1250183 – volume: 30 start-page: 767 issue: 5–6 year: 2004 ident: 4641_CR67 publication-title: Parallel Comput doi: 10.1016/j.parco.2003.12.015 – ident: 4641_CR75 doi: 10.1109/NABIC.2009.5393690 – volume: 46 start-page: 1331 issue: 10 year: 2014 ident: 4641_CR33 publication-title: Eng Optim doi: 10.1080/0305215X.2013.836640 – volume: 222 start-page: 175 issue: Supple c year: 2013 ident: 4641_CR29 publication-title: Inf Sci doi: 10.1016/j.ins.2012.08.023 – volume: 2 start-page: 5 issue: 1 year: 1998 ident: 4641_CR55 publication-title: Fac Civil Eng Belgrade – volume: 8 start-page: 1453 issue: 4 year: 2008 ident: 4641_CR22 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2007.10.013 – volume: 29 start-page: 659 issue: 3 year: 2018 ident: 4641_CR51 publication-title: J Intell Manuf doi: 10.1007/s10845-017-1294-6 – volume: 11 start-page: 652 issue: 1 year: 2011 ident: 4641_CR38 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2009.12.025 – volume: 11 start-page: 3625 issue: 4 year: 2011 ident: 4641_CR16 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2011.01.034 – volume: 1 start-page: 3 issue: 1 year: 2011 ident: 4641_CR17 publication-title: Swarm Evolut Comput doi: 10.1016/j.swevo.2011.02.002 – ident: 4641_CR73 – volume: 275 start-page: 1186 year: 2018 ident: 4641_CR86 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.09.065 – volume-title: Nature-inspired metaheuristic algorithms year: 2010 ident: 4641_CR78 – volume: 55 start-page: 314 year: 2018 ident: 4641_CR19 publication-title: Appl Math Model doi: 10.1016/j.apm.2017.08.013 – volume-title: Adaptation in natural and artificial systems year: 1992 ident: 4641_CR30 doi: 10.7551/mitpress/1090.001.0001 – volume: 9 start-page: 129 issue: 2 year: 2017 ident: 4641_CR84 publication-title: Memet Comput doi: 10.1007/s12293-016-0198-x – volume: 26 start-page: 69 year: 2012 ident: 4641_CR56 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2011.07.001 – volume: 178 start-page: 3043 issue: 15 year: 2008 ident: 4641_CR83 publication-title: Inf Sci doi: 10.1016/j.ins.2008.02.014 – ident: 4641_CR20 doi: 10.1007/978-3-662-05094-1 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 4641_CR74 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.585893 – volume: 211 start-page: 392 issue: 2 year: 2009 ident: 4641_CR82 publication-title: Appl Math Comput doi: 10.1016/j.amc.2009.01.048
SSID	ssj0004685
Score	2.5220995
Snippet	Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	11543
SubjectTerms	Algorithms Artificial Intelligence Clustering Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer simulation Data Mining and Knowledge Discovery Evolutionary algorithms Evolutionary computation Fish Functions (mathematics) Genetic algorithms Heuristic Heuristic methods Image Processing and Computer Vision Mathematical functions Optimization Original Article Particle swarm optimization Probability and Statistics in Computer Science Simulated annealing Swarm intelligence
SummonAdditionalLinks	– databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEA5aPXixPrFaJQdvuthNsrtZLyJS8VQqKBQvSzaZ2EJfdlvBf2-SZlsV7cXzbEjYmcwj8_gQOqdKJEIlLNAxgYAp48PlgoqA61hEWkotcu7AJpJWi3c6ads_uBW-rLLUiU5Rq5G0b-RXxKXoKKH0ZvwWWNQom131EBrraMNOSbDQDe3o5UtfpIPkNBGMre5h1DfNuNY5-x5qA2mbGogNnX83TEtv80eC1Nmd--p_T7yDtr3HiW_nIrKL1mC4h6olmgP2l3sfPTYdIk5PYt0runhkdMnAN2leY4GN-427MJsPdsai_2q2mnYHuDe0uXpQOP_AHlPH2ke77AA93zef7h4CD7gQSHOVp4EiDIwHRyBkALkgEMeC5pBEKbCUR6liMkwUREQb-WOJktDQWsaNFIg0eqBBD1FlOBrCEcImkEqUokrKPGV5SDmXMk6VnX6WQsx1DYXl386kn0ZuQTH62WKOsuNQZjiUOQ5lvIYuFmvG81kcK7-ul2zJ_L0ssiVPfiVT4_BE1lwnNXRZ8n1J_nuz49WbnaAtYuN0VzhYR5XpZAanaFO-T3vF5MwJ7SfGK_Lw priority: 102 providerName: ProQuest
Title	Electric fish optimization: a new heuristic algorithm inspired by electrolocation
URI	https://link.springer.com/article/10.1007/s00521-019-04641-8 https://www.proquest.com/docview/2424563233 https://www.proquest.com/docview/3218547187
Volume	32
WOSCitedRecordID	wos000500866600003&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: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1433-3058 dateEnd: 20241212 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: 20241212 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 Nature - Connect here FIRST to enable access 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/eLvHCXMwnV1LTxsxEB41gQMX0vIQoTTygRuslLW9u97eCgrqKUpTQBGXldcek5VCgpKAxL-v7XiTUkGl9uzHrsYznhnP4wM4ZVpmUmc8MinFiGtrw5WSyUiYVCZGKSNL4cEmsn5fjEb5IBSFLeps9zok6W_qdbGbe8F0rq97zE95HIkGbFl1Jxxgw_Dn7W_VkB6I0_otLqeHs1Aq8_Yer9XRxsb8Iyzqtc1V6__-8yPsBuuSfFuxwyf4gNM9aNXIDSQI8j786Hn0m0oRUy3GZGbvjYdQkPmVSGJNbTLGp1UTZyIn97N5tRw_kGrq4vKoSflCAn6O04Vu2QHcXPWuL79HAVwhUlZsl5GmHK21RjHmiKWkmKaSlZglOfJcJLnmKs40JtRYXuOZVtg1RqXdHKmyMt9lh9CczqZ4BMQ6TZnWTCtV5ryMmRBKpbl2nc5yTIVpQ1zTuFCh87gDwJgU657JnmaFpVnhaVaINpyt1zyu-m78dfZJfXRFkMFFQX1Ql1HG3hxm1rhJnGrO2nBen-Rm-P2PHf_b9M-wQ52P7pMGT6C5nD_hF9hWz8tqMe_A1kWvPxh2oDFI7jqekX8B9fzrgA
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bLwQxFD5xS3hxF-vaB56Y2G07Mx2JiLiEYENCIl5Gpz21m7CLXcSf8hu13RmL4M2D506nafv19JyeywewxLSMpY55YCKKAddWh8skk4EwkQyNUkZmwpNNxNWquLhITnrgtciFcWGVhUz0glo3lXsjX6PeRccoY5t394FjjXLe1YJCowOLQ3x5tiZba-Ngx-7vMqV7u2fb-0HOKhAoi9d2oClHq6ZQrHDETFKMIskyjMMEeSLCRHNViTWG1NhF5rFWWDZGReUEqbJgLzP7317o55yW3Sk6CS8_5GF6ClBrMbloIs7yJB2fqufeX53h7lwRkW0Xny_Crnb7xSHr77m9kf-2QqMwnGvUZKtzBMagBxvjMFKwVZBceE3A6a5n_KkrYuqtGmlaWXmbJ6GuE0mseUFq-NgpXE3kzbWdWrt2S-oNF4uAmmQvJOcMcve_6zYJ538ysSnoazQbOA3EGoqx1kwrlSU8qzAhlIoS7aq7JRgJU4JKsbupyqutO9KPm_S9TrRHRGoRkXpEpKIEK-997jq1Rn79eq6AQZrLnVbaxcC3zcwqdKFTR-ISrBY46zb_PNjM74MtwuD-2fFRenRQPZyFIereJHyQ5Bz0tR8ecR4G1FO73npY8AeGwNVf4-8NFelR5Q
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dT9swED-xMk28UPaBKBTmh71tEY3tJA5vCFqBNlVM-1DfIsc-00iQojZF4r_HdpPSoTJp2rM_Ep3vfHe-jx_AJ6ZlInXCAxNTDLi2NlwumQyEiWVklDIyFx5sIhkOxWiUXq1U8fts9yYkuahpcF2ayur4TpvjZeGbe810brB72I95GIhXsMldIr3z13_8XqmM9KCc1odx-T2c1WUz6_f4UzU92ZvPQqRe8wza___PO7BdW53kdMEmb2EDy3fQbhAdSC3g7-F736PiFIqYYjYmE3uf3NaFmidEEmuCkzHOF82diby5nkyLanxLitLF61GT_IHUuDpOR7plH-DXoP_z7CKoQRcCZcW5CjTlaK04iiFHzCXFOJYsxyRKkaciSjVXYaIxosbyIE-0wp4xKu6lSJW9C3psF1rlpMQ9INaZSrRmWqk85XnIhFAqTrXrgJZiLEwHwobemao7kjtgjJts2UvZ0yyzNMs8zTLRgc_LNXeLfhx_nd1tjjGrZXOWUR_sZZSxtcPMGj2RU9lJB740p_o0_PLH9v9t-kd4c3U-yL5dDr8ewBZ1brzPK-xCq5rO8RBeq_uqmE2PPEc_Arth9K4
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=Electric+fish+optimization%3A+a+new+heuristic+algorithm+inspired+by+electrolocation&rft.jtitle=Neural+computing+%26+applications&rft.au=Yilmaz%2C+Selim&rft.au=Sen%2C+Sevil&rft.date=2020-08-01&rft.pub=Springer+London&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=32&rft.issue=15&rft.spage=11543&rft.epage=11578&rft_id=info:doi/10.1007%2Fs00521-019-04641-8&rft.externalDocID=10_1007_s00521_019_04641_8
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