Enhanced Arithmetic Optimization Algorithm for Parameter Estimation of PID Controller
The Proportional-Integral-Derivative (PID) controller is a key component in most engineering applications. The main disadvantage of PID is the selection of the best values for its parameters using traditional methods that do not achieve the best response. In this work, the recently released empirica...
Saved in:
Published in | Arabian journal for science and engineering Vol. 48; no. 2; pp. 2191 - 2205 |
---|---|
Main Author | |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2023
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 2193-567X 1319-8025 2191-4281 |
DOI | 10.1007/s13369-022-07136-2 |
Cover
Abstract | The Proportional-Integral-Derivative (PID) controller is a key component in most engineering applications. The main disadvantage of PID is the selection of the best values for its parameters using traditional methods that do not achieve the best response. In this work, the recently released empirical identification algorithm that is the Arithmetic Optimization Algorithm (AOA) was used to determine the best values of the PID parameters. AOA was selected due to its effective exploration ability. Unfortunately, AOA cannot achieve the best parameter values due to its poor exploitation of search space. Hence, the performance of the AOA exploit is improved by combining it with the Harris Hawk Optimization (HHO) algorithm which has an efficient exploit mechanism. In addition, avoidance of trapping in the local lower bounds of AOA–HHO is enhanced by the inclusion of perturbation and mutation factors. The proposed AOA–HHO algorithm is tested when choosing the best values for PID parameters to control two engineering applications namely DC motor regulation and three fluid level sequential tank systems. AOA–HHO has superiority over AOA and comparative algorithms. |
---|---|
AbstractList | The Proportional-Integral-Derivative (PID) controller is a key component in most engineering applications. The main disadvantage of PID is the selection of the best values for its parameters using traditional methods that do not achieve the best response. In this work, the recently released empirical identification algorithm that is the Arithmetic Optimization Algorithm (AOA) was used to determine the best values of the PID parameters. AOA was selected due to its effective exploration ability. Unfortunately, AOA cannot achieve the best parameter values due to its poor exploitation of search space. Hence, the performance of the AOA exploit is improved by combining it with the Harris Hawk Optimization (HHO) algorithm which has an efficient exploit mechanism. In addition, avoidance of trapping in the local lower bounds of AOA–HHO is enhanced by the inclusion of perturbation and mutation factors. The proposed AOA–HHO algorithm is tested when choosing the best values for PID parameters to control two engineering applications namely DC motor regulation and three fluid level sequential tank systems. AOA–HHO has superiority over AOA and comparative algorithms. |
Author | Issa, Mohamed |
Author_xml | – sequence: 1 givenname: Mohamed orcidid: 0000-0001-8524-8379 surname: Issa fullname: Issa, Mohamed email: Mamohamedali@eng.zu.edu.eg organization: Computer and Systems Department, Faculty of Engineering, Zagazig University, Faculty of Computer Science, NAHDA University in Beni-Suef |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36042895$$D View this record in MEDLINE/PubMed |
BookMark | eNp9kc1qGzEUhUVxaH6aF-iiDHQ9ja6kGY02BeO6TSCQLBLITmg0kq0yllxpXEievrLHSZMsvJLgfufcwz2naOKDNwh9BvwNMOYXCSitRYkJKTEHWpfkAzohIKBkpIHJ7k_LquYPx-g8Jddi1lBRAdCP6JjWOFOiOkH3c79UXpuumEY3LFdmcLq4WQ9u5Z7U4IIvpv0i7EaFDbG4VVFlyMRinjI0IsEWt1c_ilnwQwx9b-IndGRVn8z5_j1D9z_nd7PL8vrm19Vsel1qxtlQCt01tSCAAbPOqk7xtlLWAMcc09YYCxqLVjcVsbzpWtHVCgtWA6OWV9wAPUPfR9_1pl2ZTpscQPVyHXOy-CiDcvLtxLulXIS_UjCApqLZ4OveIIY_G5MG-Ttsos-ZJeGcVTUGQjL15fWaF__nM2agGQEdQ0rRWKndsLtN3up6CVhuS5NjaTKXJnelya03eSd9dj8ooqMoZdgvTPwf-4DqH1T0qmo |
CitedBy_id | crossref_primary_10_1002_adc2_70005 crossref_primary_10_3390_electronics11193143 crossref_primary_10_1007_s00521_023_09261_x crossref_primary_10_1038_s41598_023_32793_0 crossref_primary_10_31763_ijrcs_v3i3_1116 crossref_primary_10_1007_s00521_024_10805_y crossref_primary_10_1007_s11760_023_02684_y crossref_primary_10_1007_s00521_024_10313_z crossref_primary_10_1016_j_asoc_2024_111601 crossref_primary_10_1038_s41598_025_87695_0 crossref_primary_10_1007_s11042_023_17084_0 crossref_primary_10_1016_j_ijepes_2024_110266 crossref_primary_10_1002_rnc_7304 crossref_primary_10_1007_s00521_023_09023_9 crossref_primary_10_1109_ACCESS_2023_3319596 crossref_primary_10_1016_j_egyr_2024_06_019 crossref_primary_10_3390_math12203221 crossref_primary_10_1016_j_prime_2023_100295 crossref_primary_10_3390_biomimetics8040348 crossref_primary_10_1016_j_isatra_2025_01_003 crossref_primary_10_1016_j_jenvman_2025_124238 crossref_primary_10_1142_S1793962324500296 crossref_primary_10_3934_math_2024654 crossref_primary_10_1007_s13369_024_09051_0 crossref_primary_10_1038_s41598_025_85273_y crossref_primary_10_1016_j_aej_2024_04_021 crossref_primary_10_1016_j_jestch_2025_101982 crossref_primary_10_1007_s13369_023_08360_0 crossref_primary_10_1002_cjce_25343 |
Cites_doi | 10.1109/ACCESS.2019.2902306 10.1109/IKT54664.2021.9685429 10.1016/j.asoc.2021.107197 10.1007/978-3-030-70542-8_28 10.1007/978-3-030-56689-0_8 10.1016/j.eswa.2018.01.019 10.1016/j.neucom.2015.11.018 10.1109/ACCESS.2019.2905961 10.5772/652 10.1109/ICCPCT.2016.7530150 10.4018/978-1-5225-2229-4.ch018 10.1007/s00500-020-04842-7 10.1007/s00500-018-3586-y 10.31814/stce.huce(nuce)2022-16(2)-03 10.1016/j.compchemeng.2019.106656 10.1007/978-3-030-69744-0_21 10.1007/s12652-022-03724-0 10.1016/j.asoc.2020.106683 10.1007/s13369-022-06605-y 10.1016/j.engappai.2022.104753 10.1016/j.eswa.2021.116432 10.1016/j.asoc.2015.10.041 10.1007/s00542-018-3920-4 10.1109/ISMSIT.2019.8932907 10.3390/electronics10222834 10.1109/4235.585893 10.1016/j.eswa.2017.02.035 10.1145/2001858.2002123 10.1016/j.asoc.2017.06.033 10.1109/SIS.2007.367959 10.1007/s00366-020-01028-5 10.1002/9780470496916 10.1115/1.4019264 10.1016/j.rser.2016.03.049 10.3390/pr9071155 10.1016/j.future.2019.02.028 10.1007/978-3-319-74690-6_11 10.1109/TEC.2003.821821 10.1016/j.asej.2018.07.005 10.1016/j.cma.2020.113609 10.1016/j.matcom.2021.08.016 10.1109/UPEC50034.2021.9548204 10.1007/s12065-022-00711-4 10.1016/j.energy.2019.116025 10.1371/journal.pone.0255703 10.1016/j.ins.2019.04.022 10.1109/ACCESS.2021.3085529 10.1016/j.eswa.2019.112976 10.1016/j.knosys.2015.12.022 10.1007/978-3-030-02357-7_20 10.1007/s00500-020-05349-x 10.1016/j.enconman.2020.113211 10.3390/rs11121421 10.1016/j.eswa.2021.116063 10.1016/j.eswa.2019.113103 10.1007/s10489-021-03125-4 10.3390/s19163590 10.1016/j.knosys.2015.07.006 10.1016/j.jclepro.2019.118778 10.1109/TMAG.2011.2176108 10.3906/elk-1612-277 10.1109/MPS52805.2021.9492572 10.1155/2022/3987494 10.1016/j.ijepes.2015.11.010 10.1016/j.est.2022.104343 10.1016/j.asoc.2015.03.035 10.1155/2021/9114113 10.3390/math9182321 10.1109/ACCESS.2022.3146374 10.1007/s10489-016-0848-1 10.3390/pr9101774 10.1007/s00500-022-06873-8 10.1109/ACCESS.2019.2921545 10.1007/978-3-030-04792-4_8 |
ContentType | Journal Article |
Copyright | The Author(s) 2022 The Author(s) 2022. The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: The Author(s) 2022 – notice: The Author(s) 2022. – notice: The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | C6C AAYXX CITATION NPM 5PM |
DOI | 10.1007/s13369-022-07136-2 |
DatabaseName | Springer Nature OA Free Journals (WRLC) CrossRef PubMed PubMed Central (Full Participant titles) |
DatabaseTitle | CrossRef PubMed |
DatabaseTitleList | CrossRef PubMed |
Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals (WRLC) url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 2191-4281 |
EndPage | 2205 |
ExternalDocumentID | PMC9411853 36042895 10_1007_s13369_022_07136_2 |
Genre | Journal Article |
GrantInformation_xml | – fundername: Zagazig University – fundername: ; |
GroupedDBID | -EM 0R~ 203 2KG 406 AAAVM AACDK AAHNG AAIAL AAJBT AANZL AARHV AASML AATNV AATVU AAUYE AAYTO AAYZH ABAKF ABDBF ABDZT ABECU ABFTD ABFTV ABJNI ABJOX ABKCH ABMQK ABQBU ABSXP ABTEG ABTKH ABTMW ABXPI ACAOD ACBXY ACDTI ACHSB ACMDZ ACMLO ACOKC ACPIV ACUHS ACZOJ ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEJRE AEMSY AEOHA AESKC AEVLU AEXYK AFBBN AFLOW AFQWF AGAYW AGJBK AGMZJ AGQEE AGQMX AGRTI AHAVH AHBYD AHSBF AIAKS AIGIU AILAN AITGF AJBLW AJRNO AJZVZ ALFXC ALMA_UNASSIGNED_HOLDINGS AMXSW AMYLF AOCGG AXYYD BGNMA C6C CSCUP DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD ESX FERAY FIGPU FINBP FNLPD FSGXE GGCAI GQ6 GQ7 H13 HG6 I-F IKXTQ IWAJR J-C JBSCW JZLTJ L8X LLZTM M4Y MK~ NPVJJ NQJWS NU0 O9J PT4 ROL RSV SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG TUS UOJIU UTJUX UZXMN VFIZW Z5O Z7R Z7V Z7X Z7Y Z7Z Z81 Z83 Z85 Z88 ZMTXR ~8M AAPKM AAYXX ABBRH ABDBE ABFSG ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION NPM 06D 0VY 23M 29~ 2KM 30V 408 5GY 96X AAJKR AARTL AAYIU AAYQN AAZMS ABTHY ACGFS ACKNC ADHHG ADHIR AEGNC AEJHL AENEX AEPYU AETCA AFWTZ AFZKB AGDGC AGWZB AGYKE AHYZX AIIXL AMKLP AMYQR ANMIH AYJHY ESBYG FFXSO FRRFC FYJPI GGRSB GJIRD GX1 HMJXF HRMNR HZ~ I0C IXD J9A KOV O93 OVT P9P R9I RLLFE S27 S3B SEG SHX T13 U2A UG4 VC2 W48 WK8 ~A9 5PM ABRTQ |
ID | FETCH-LOGICAL-c474t-9cd869210104dfada7b5afe170703beef1c09bc852f78db9d6a0946143f757e13 |
IEDL.DBID | C6C |
ISSN | 2193-567X 1319-8025 |
IngestDate | Thu Aug 21 13:49:53 EDT 2025 Mon Jun 30 08:59:58 EDT 2025 Thu Jan 02 22:54:08 EST 2025 Tue Jul 01 01:34:27 EDT 2025 Thu Apr 24 23:00:14 EDT 2025 Fri Feb 21 02:45:05 EST 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 2 |
Keywords | Arithmetic optimization algorithm (AOA) PID controller Harris Hawk optimization algorithm (HHO) |
Language | English |
License | The Author(s) 2022. Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c474t-9cd869210104dfada7b5afe170703beef1c09bc852f78db9d6a0946143f757e13 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ORCID | 0000-0001-8524-8379 |
OpenAccessLink | https://doi.org/10.1007/s13369-022-07136-2 |
PMID | 36042895 |
PQID | 2774560122 |
PQPubID | 2044268 |
PageCount | 15 |
ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_9411853 proquest_journals_2774560122 pubmed_primary_36042895 crossref_citationtrail_10_1007_s13369_022_07136_2 crossref_primary_10_1007_s13369_022_07136_2 springer_journals_10_1007_s13369_022_07136_2 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2023-02-01 |
PublicationDateYYYYMMDD | 2023-02-01 |
PublicationDate_xml | – month: 02 year: 2023 text: 2023-02-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Berlin/Heidelberg |
PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
PublicationTitle | Arabian journal for science and engineering |
PublicationTitleAbbrev | Arab J Sci Eng |
PublicationTitleAlternate | Arab J Sci Eng |
PublicationYear | 2023 |
Publisher | Springer Berlin Heidelberg Springer Nature B.V |
Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
References | RamachandranMA hybrid grasshopper optimization algorithm and harris hawks optimizer for combined heat and power economic dispatch problemEng. Appl. Artif. Intell.2022111 IssaMA biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19Expert Syst. Appl.2022189 JordehiARParameter estimation of solar photovoltaic (PV) cells: A reviewRenew. Sustain. Energy Rev.201661354371 ChenHParameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic driftsJ. Clean. Prod.2020244 Abd ElazizMImproving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithmSoft. Comput.202024191488514905 KharrichMAn improved arithmetic optimization algorithm for design of a microgrid with energy storage system: case study of El Kharga Oasis, EgyptJ. Energy Storage202251104343 PremkumarMA new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: diversity analysis and validationsIEEE Access202198426384295 RazmjooyNSpeed control of a DC motor using pid controller based on improved whale optimization algorithmMetaheuristics and Optimization in Computer and Electrical Engineering2021Springer153167 Issa, M.; Hassanien, A.E.: Multiple sequence alignment optimization using meta-heuristic techniques. In: Handbook of Research on Machine Learning Innovations and Trends. IGI Global. p. 409–423 (2017) EkinciSHekimoğluBIzciDOpposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motorEng Sci Technol Int J2021242331342 LiuHDingGWangBBare-bones particle swarm optimization with disruption operatorAppl. Math. Comput.201423810612232096201334.90213 Kennedy: Particle swarm optimization. Neural Netw (1995) ZhangQChaos-induced and mutation-driven schemes boosting salp chains-inspired optimizersIEEE Access201973124331261 KhodadadiNSnaselVMirjaliliSDynamic arithmetic optimization algorithm for truss optimization under natural frequency constraintsIEEE Access2022101618816208 ZhengRDeep ensemble of slime mold algorithm and arithmetic optimization algorithm for global optimizationProcesses20219101774 Mahboob, A.S.; Moghaddam, M.R.O.; Yousefi. S.: AOV-IDS: arithmetic optimizer with voting classifier for intrusion detection system. In: 2021 12th International Conference on Information and Knowledge Technology (IKT). IEEE (2021) Bora, T.C.; L.d.S. Coelho, and L. Lebensztajn, Bat-inspired optimization approach for the brushless DC wheel motor problem. IEEE Transactions on magnetics, 2012. 48(2): p. 947–950. Singh, N. et al.;: HSSAHHO: a novel hybrid Salp swarm-Harris hawks optimization algorithm for complex engineering problems. J Ambient Intell Humanized Comput, p. 1–37 (2022) Wang, H. et al.: Opposition-based particle swarm algorithm with Cauchy mutation. In: 2007 IEEE Congress on Evolutionary Computation. IEEE (2007) WolpertDHMacreadyWGNo free lunch theorems for optimizationIEEE Trans. Evol. Comput.1997116782 GaingZ-LA particle swarm optimization approach for optimum design of PID controller in AVR systemIEEE Trans. Energy Convers.2004192384391 Abd Elaziz, M. et al.: IoT workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. Comput. Intel. Neurosci (2021) Ziegler, J.G.; Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 64(11) (1942) Zellagui, M. et al. Arithmetic optimization algorithm for optimal installation of DSTATCOM into distribution system based on various voltage stability indices. In: 2021 9th International Conference on Modern Power Systems (MPS). IEEE (2021) AgarwalJAnalysis of grey wolf optimizer based fractional order PID controller in speed control of DC motorMicrosyst. Technol.2018241249975006 ZhangYBoosted binary Harris hawks optimizer and feature selectionEng. Comput.202137437413770 Issa, M. et al. Pairwise Global Sequence Alignment Using Sine-Cosine Optimization Algorithm. In: International Conference on Advanced Machine Learning Technologies and Applications. Springer (2018) Talbi E.-G.: Metaheuristics: From Design to Implementation. vol. 74. John Wiley & Sons, London (2009) ZhangXGaussian mutational chaotic fruit fly-built optimization and feature selectionExpert Syst. Appl.2020141 BendjeghabaOContinuous firefly algorithm for optimal tuning of PID controller in AVR systemJ. Electr. Eng.201465144 HamidzadehJFeature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operatorSoft. Comput.202125429112933 AgarwalJParmarGGuptaRApplication of sine cosine algorithm in optimal control of DC motor and robustness analysisWulfenia J201724117795 Harwit, M.: Astrophysical Concepts. Springer Science & Business Media, Berlin (2006) Mahajan, S. et al.: Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks. Soft Comput, p. 1–19 (2022) Issa, M.: Sequence analysis algorithms for bioinformatics application. GRIN Verlag (2014) Issa, M.; Abd Elaziz, M.: Analyzing COVID-19 virus based on enhanced fragmented biological Local Aligner using improved ions motion optimization algorithm. Appl Soft Comput, p. 106683 (2020) ChatterjeeSMukherjeeVPID controller for automatic voltage regulator using teaching–learning based optimization techniqueInt. J. Electr. Power Energy Syst.201677418429 Abbassi, A. et al.: Improved arithmetic optimization algorithm for parameters extraction of photovoltaic solar cell single-diode model. Arab. J. Sci. Eng.; p. 1–17 (2022) MoharamAEl-HosseiniMAAliHADesign of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengersAppl. Soft Comput.201638727737 SapreSMiniSOpposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling for global optimizationSoft. Comput.2019231560236041 IssaMPID controller tuning parameters using meta-heuristics algorithms: comparative analysisMachine Learning Paradigms: Theory and Application2019Springer413430 Malibari, A.A. et al.: Arithmetic optimization with retinanet model for motor imagery classification on brain computer interface. J. Healthcare Eng. (2022) Ekinci, S. et al.: Speed control of DC motor using improved sine cosine algorithm based PID controller. In: 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE (2019) Ogata, K.; Yang, Y.: Modern control engineering. Vol. 4. Prentice hall India, Prentice (2002) AbualigahLA novel evolutionary arithmetic optimization algorithm for multilevel thresholding segmentation of covid-19 ct imagesProcesses2021971155 Do, D.T. et al.: A hybrid arithmetic optimization algorithm and differential evolution for optimization of truss structures subjected to frequency constraints. J. Sci. Technol. Civil Eng. (STCE)-HUCE (2022) NematollahiAFRahiminejadAVahidiBA novel physical based meta-heuristic optimization method known as lightning attachment procedure optimizationAppl. Soft Comput.201759596621 IssaMSamnAPassive vehicle suspension system optimization using Harris Hawk Optimization algorithmMath. Comput. Simul.2022191328345430945407431708 Mansour, T.: PID Control: Implementation and Tuning. BoD–Books on Demand (2011) Hao, W.-K. et al.: Arithmetic optimization algorithm based on elementary function disturbance for solving economic load dispatch problem in power system. Appl. Intell.; p. 1–27 (2022) Roy, A.; Srivastava, S.: Design of optimal PIλDδ controller for speed control of DC motor using constrained particle swarm optimization. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE (2016) EweesAABoosting arithmetic optimization algorithm with genetic algorithm operators for feature selection: case study on cox proportional hazards modelMathematics20219182321 AgushakaJOEzugwuAEAdvanced arithmetic optimization algorithm for solving mechanical engineering design problemsPLoS ONE2021168 BansalJCFarswanPA novel disruption in biogeography-based optimization with application to optimal power flow problemAppl. Intell.2017463590615 BaoXJiaHLangCA novel hybrid harris hawks optimization for color image multilevel thresholding segmentationIEEE Access201977652976546 IssaMASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignmentExpert Syst. Appl.2018995670 Garzon Rivera, O.D. et al.: Application of the Arithmetic Optimization Algorithm to Solve the Optimal Power Flow Problem in Direct Current Networks. Available at SSRN, 4069702. WangG-GOpposition-based krill herd algorithm with Cauchy mutation and position clampingNeurocomputing2016177147157 FathyAOptimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithmEnergy2019188 HeidariAAHarris hawks optimization: algorithm and applicationsFutur. Gener. Comput. Syst.201997849872 MirjaliliSSCA: a sine cosine algorithm for solving optimization problemsKnowl.Based Syst.201696120133 MirjaliliSMoth-flame optimization algorithm: a novel nature-inspired heuristic paradigmKnowl.Based Syst.201589228249 NeggazNBoosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selectionExpert Syst. Appl.2020145 SalgotraRSinghUApplication of mutation operators to flower pollination algorithmExp. Syst. Appl.201779112129 IssaMHelmiATwo layer hybrid scheme of IMO and PSO for optimization of local aligner: COVID-19 as a case studyArtificial Intelligence for COVID-192021Springer363381 Pan, J.-S. et al.: A Multi-objective ions motion optimization for robot path planning. In: International conference on engineering research and applications. Springer (2018) KhubalkarSModeling and control of a permanent-magnet brushless DC motor drive using a fractional order proportional-integral-derivative controllerTurk. J. Electr. Eng. Comput. Sci.201725542234241 IssaMExpeditious Covid-19 similarity measure tool b M Issa (7136_CR16) 2021 7136_CR64 S Khubalkar (7136_CR26) 2017; 25 M Issa (7136_CR19) 2019 S Mirjalili (7136_CR5) 2016; 96 B Javidy (7136_CR7) 2015; 32 M Issa (7136_CR17) 2022; 189 N Neggaz (7136_CR69) 2020; 145 N Razmjooy (7136_CR28) 2021 DT Bui (7136_CR61) 2019; 19 7136_CR29 AF Nematollahi (7136_CR8) 2017; 59 A Fathy (7136_CR72) 2019; 188 H Liu (7136_CR67) 2014; 238 JO Agushaka (7136_CR52) 2021; 16 7136_CR66 7136_CR21 7136_CR25 7136_CR53 S Mirjalili (7136_CR9) 2015; 89 Y Zhang (7136_CR57) 2021; 37 L Abualigah (7136_CR36) 2021; 376 D Potnuru (7136_CR30) 2019; 10 X Bao (7136_CR59) 2019; 7 H Jia (7136_CR75) 2019; 11 Q Zhang (7136_CR74) 2019; 7 R Zheng (7136_CR50) 2021; 9 X Zhang (7136_CR78) 2020; 141 H Chen (7136_CR58) 2020; 244 7136_CR18 O Bendjeghaba (7136_CR31) 2014; 65 M Kharrich (7136_CR42) 2022; 51 JC Bansal (7136_CR70) 2017; 46 N Khodadadi (7136_CR39) 2022; 10 Z-L Gaing (7136_CR23) 2004; 19 7136_CR13 7136_CR55 7136_CR6 7136_CR10 7136_CR54 7136_CR3 7136_CR4 7136_CR1 7136_CR15 AA Ewees (7136_CR51) 2021; 9 7136_CR2 M Issa (7136_CR11) 2018; 99 7136_CR14 7136_CR82 7136_CR81 S Sapre (7136_CR80) 2019; 23 M Premkumar (7136_CR37) 2021; 9 R Salgotra (7136_CR73) 2017; 79 7136_CR41 J Agarwal (7136_CR32) 2018; 24 7136_CR40 G-G Wang (7136_CR79) 2016; 177 M Issa (7136_CR12) 2021; 104 DH Wolpert (7136_CR35) 1997; 1 C Zhong (7136_CR65) 2022; 192 L Abualigah (7136_CR38) 2021; 9 M Abd Elaziz (7136_CR71) 2020; 24 A Moharam (7136_CR34) 2016; 38 AR Jordehi (7136_CR20) 2016; 61 EH Houssein (7136_CR60) 2020; 133 M Issa (7136_CR24) 2021 A Sharma (7136_CR49) 2021; 10 7136_CR46 7136_CR45 7136_CR44 AA Heidari (7136_CR56) 2019; 97 7136_CR43 7136_CR48 M Issa (7136_CR22) 2022; 191 7136_CR47 Y Liu (7136_CR62) 2020; 223 J Hamidzadeh (7136_CR68) 2021; 25 S Ekinci (7136_CR27) 2021; 24 M Ramachandran (7136_CR63) 2022; 111 J Agarwal (7136_CR83) 2017; 24 S Chatterjee (7136_CR33) 2016; 77 Y Xu (7136_CR76) 2019; 492 7136_CR77 B Hekimoğlu (7136_CR84) 2019; 7 |
References_xml | – reference: AbualigahLA novel evolutionary arithmetic optimization algorithm for multilevel thresholding segmentation of covid-19 ct imagesProcesses2021971155 – reference: AbualigahLThe arithmetic optimization algorithmComput. Methods Appl. Mech. Eng.2021376419929907340412 – reference: LiuHDingGWangBBare-bones particle swarm optimization with disruption operatorAppl. Math. Comput.201423810612232096201334.90213 – reference: AgushakaJOEzugwuAEAdvanced arithmetic optimization algorithm for solving mechanical engineering design problemsPLoS ONE2021168 – reference: Talbi E.-G.: Metaheuristics: From Design to Implementation. vol. 74. John Wiley & Sons, London (2009) – reference: SalgotraRSinghUApplication of mutation operators to flower pollination algorithmExp. Syst. Appl.201779112129 – reference: Roy, A.; Srivastava, S.: Design of optimal PIλDδ controller for speed control of DC motor using constrained particle swarm optimization. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE (2016) – reference: IssaMPerformance optimization of pid controller based on parameters estimation using meta-heuristic techniques: a comparative studyMetaheuristics in Machine Learning: Theory and Applications2021Springer691709 – reference: PotnuruDMaryKABabuCSExperimental implementation of flower pollination algorithm for speed controller of a BLDC motorAin Shams Eng. J.2019102287295 – reference: EweesAABoosting arithmetic optimization algorithm with genetic algorithm operators for feature selection: case study on cox proportional hazards modelMathematics20219182321 – reference: NeggazNBoosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selectionExpert Syst. Appl.2020145 – reference: Pan, J.-S. et al.: A Multi-objective ions motion optimization for robot path planning. In: International conference on engineering research and applications. Springer (2018) – reference: ChatterjeeSMukherjeeVPID controller for automatic voltage regulator using teaching–learning based optimization techniqueInt. J. Electr. Power Energy Syst.201677418429 – reference: Mansour, T.: PID Control: Implementation and Tuning. BoD–Books on Demand (2011) – reference: MoharamAEl-HosseiniMAAliHADesign of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengersAppl. Soft Comput.201638727737 – reference: GaingZ-LA particle swarm optimization approach for optimum design of PID controller in AVR systemIEEE Trans. Energy Convers.2004192384391 – reference: ZhangYBoosted binary Harris hawks optimizer and feature selectionEng. Comput.202137437413770 – reference: Wang, H. et al.: Opposition-based particle swarm algorithm with Cauchy mutation. In: 2007 IEEE Congress on Evolutionary Computation. IEEE (2007) – reference: Hansen, N.; Auger. A.: CMA-ES: evolution strategies and covariance matrix adaptation. In: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (2011)s – reference: Ziegler, J.G.; Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 64(11) (1942) – reference: Khalilpour, M. et al.: Optimal control of DC motor using invasive weed optimization (IWO) algorithm. In: Majlesi Conference on Electrical Engineering, Majlesi New Town, Isfahan, Iran (2011) – reference: JavidyBHatamlouAMirjaliliSIons motion algorithm for solving optimization problemsAppl. Soft Comput.2015327279 – reference: Zellagui, M. et al. Arithmetic optimization algorithm for optimal installation of DSTATCOM into distribution system based on various voltage stability indices. In: 2021 9th International Conference on Modern Power Systems (MPS). IEEE (2021) – reference: IssaMASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignmentExpert Syst. Appl.2018995670 – reference: ZhengRDeep ensemble of slime mold algorithm and arithmetic optimization algorithm for global optimizationProcesses20219101774 – reference: SapreSMiniSOpposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling for global optimizationSoft. Comput.2019231560236041 – reference: MirjaliliSSCA: a sine cosine algorithm for solving optimization problemsKnowl.Based Syst.201696120133 – reference: BaoXJiaHLangCA novel hybrid harris hawks optimization for color image multilevel thresholding segmentationIEEE Access201977652976546 – reference: RazmjooyNSpeed control of a DC motor using pid controller based on improved whale optimization algorithmMetaheuristics and Optimization in Computer and Electrical Engineering2021Springer153167 – reference: Aydemir, S.B.: A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evol Intell, p. 1–16 (2022) – reference: EkinciSHekimoğluBIzciDOpposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motorEng Sci Technol Int J2021242331342 – reference: Abd Elaziz, M. et al.: IoT workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. Comput. Intel. Neurosci (2021) – reference: KharrichMAn improved arithmetic optimization algorithm for design of a microgrid with energy storage system: case study of El Kharga Oasis, EgyptJ. Energy Storage202251104343 – reference: Issa, M.; Abd Elaziz, M.: Analyzing COVID-19 virus based on enhanced fragmented biological Local Aligner using improved ions motion optimization algorithm. Appl Soft Comput, p. 106683 (2020) – reference: SharmaAA Novel opposition-based arithmetic optimization algorithm for parameter extraction of PEM fuel cellElectronics202110222834 – reference: IssaMPID controller tuning parameters using meta-heuristics algorithms: comparative analysisMachine Learning Paradigms: Theory and Application2019Springer413430 – reference: ZhangXGaussian mutational chaotic fruit fly-built optimization and feature selectionExpert Syst. Appl.2020141 – reference: Hao, W.-K. et al.: Arithmetic optimization algorithm based on elementary function disturbance for solving economic load dispatch problem in power system. Appl. Intell.; p. 1–27 (2022) – reference: WangG-GOpposition-based krill herd algorithm with Cauchy mutation and position clampingNeurocomputing2016177147157 – reference: JiaHDynamic harris hawks optimization with mutation mechanism for satellite image segmentationRemote sensing201911121421 – reference: Bora, T.C.; L.d.S. Coelho, and L. Lebensztajn, Bat-inspired optimization approach for the brushless DC wheel motor problem. IEEE Transactions on magnetics, 2012. 48(2): p. 947–950. – reference: Malibari, A.A. et al.: Arithmetic optimization with retinanet model for motor imagery classification on brain computer interface. J. Healthcare Eng. (2022) – reference: Ogata, K.; Yang, Y.: Modern control engineering. Vol. 4. Prentice hall India, Prentice (2002) – reference: BansalJCFarswanPA novel disruption in biogeography-based optimization with application to optimal power flow problemAppl. Intell.2017463590615 – reference: KhodadadiNSnaselVMirjaliliSDynamic arithmetic optimization algorithm for truss optimization under natural frequency constraintsIEEE Access2022101618816208 – reference: Ahmadi, B. et al.: The Arithmetic optimization algorithm for optimal energy resource planning. In 2021 56th International Universities Power Engineering Conference (UPEC). IEEE (2021) – reference: AgarwalJAnalysis of grey wolf optimizer based fractional order PID controller in speed control of DC motorMicrosyst. Technol.2018241249975006 – reference: ChenHParameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic driftsJ. Clean. Prod.2020244 – reference: Ekinci, S. et al.: Speed control of DC motor using improved sine cosine algorithm based PID controller. In: 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE (2019) – reference: Do, D.T. et al.: A hybrid arithmetic optimization algorithm and differential evolution for optimization of truss structures subjected to frequency constraints. J. Sci. Technol. Civil Eng. (STCE)-HUCE (2022) – reference: AgarwalJParmarGGuptaRApplication of sine cosine algorithm in optimal control of DC motor and robustness analysisWulfenia J201724117795 – reference: Abbassi, A. et al.: Improved arithmetic optimization algorithm for parameters extraction of photovoltaic solar cell single-diode model. Arab. J. Sci. Eng.; p. 1–17 (2022) – reference: Issa, M.: Sequence analysis algorithms for bioinformatics application. GRIN Verlag (2014) – reference: IssaMA biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19Expert Syst. Appl.2022189 – reference: Abd ElazizMImproving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithmSoft. Comput.202024191488514905 – reference: LiuYHorizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic modelsEnergy Convers. Manage.2020223 – reference: Kennedy: Particle swarm optimization. Neural Netw (1995) – reference: Issa, M.; Hassanien, A.E.: Multiple sequence alignment optimization using meta-heuristic techniques. In: Handbook of Research on Machine Learning Innovations and Trends. IGI Global. p. 409–423 (2017) – reference: HeidariAAHarris hawks optimization: algorithm and applicationsFutur. Gener. Comput. Syst.201997849872 – reference: FathyAOptimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithmEnergy2019188 – reference: MirjaliliSMoth-flame optimization algorithm: a novel nature-inspired heuristic paradigmKnowl.Based Syst.201589228249 – reference: IssaMHelmiATwo layer hybrid scheme of IMO and PSO for optimization of local aligner: COVID-19 as a case studyArtificial Intelligence for COVID-192021Springer363381 – reference: BuiDTA novel swarm intelligence—Harris hawks optimization for spatial assessment of landslide susceptibilitySensors201919163590 – reference: BendjeghabaOContinuous firefly algorithm for optimal tuning of PID controller in AVR systemJ. Electr. Eng.201465144 – reference: ZhongCLiGComprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problemsExpert Syst. Appl.2022192 – reference: HousseinEHA novel hybrid Harris hawks optimization and support vector machines for drug design and discoveryComput. Chem. Eng.2020133 – reference: Issa, M. et al. Pairwise Global Sequence Alignment Using Sine-Cosine Optimization Algorithm. In: International Conference on Advanced Machine Learning Technologies and Applications. Springer (2018) – reference: NematollahiAFRahiminejadAVahidiBA novel physical based meta-heuristic optimization method known as lightning attachment procedure optimizationAppl. Soft Comput.201759596621 – reference: JordehiARParameter estimation of solar photovoltaic (PV) cells: A reviewRenew. Sustain. Energy Rev.201661354371 – reference: PremkumarMA new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: diversity analysis and validationsIEEE Access202198426384295 – reference: HekimoğluBOptimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithmIEEE Access201973810038114 – reference: IssaMExpeditious Covid-19 similarity measure tool based on consolidated SCA algorithm with mutation and opposition operatorsAppl. Soft Comput.2021104 – reference: Harwit, M.: Astrophysical Concepts. Springer Science & Business Media, Berlin (2006) – reference: WolpertDHMacreadyWGNo free lunch theorems for optimizationIEEE Trans. Evol. Comput.1997116782 – reference: ZhangQChaos-induced and mutation-driven schemes boosting salp chains-inspired optimizersIEEE Access201973124331261 – reference: Singh, N. et al.;: HSSAHHO: a novel hybrid Salp swarm-Harris hawks optimization algorithm for complex engineering problems. J Ambient Intell Humanized Comput, p. 1–37 (2022) – reference: XuYEnhanced Moth-flame optimizer with mutation strategy for global optimizationInf. Sci.20194921812033941398 – reference: IssaMSamnAPassive vehicle suspension system optimization using Harris Hawk Optimization algorithmMath. Comput. Simul.2022191328345430945407431708 – reference: Garzon Rivera, O.D. et al.: Application of the Arithmetic Optimization Algorithm to Solve the Optimal Power Flow Problem in Direct Current Networks. Available at SSRN, 4069702. – reference: HamidzadehJFeature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operatorSoft. Comput.202125429112933 – reference: KhubalkarSModeling and control of a permanent-magnet brushless DC motor drive using a fractional order proportional-integral-derivative controllerTurk. J. Electr. Eng. Comput. Sci.201725542234241 – reference: Mahajan, S. et al.: Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks. Soft Comput, p. 1–19 (2022) – reference: Mahboob, A.S.; Moghaddam, M.R.O.; Yousefi. S.: AOV-IDS: arithmetic optimizer with voting classifier for intrusion detection system. In: 2021 12th International Conference on Information and Knowledge Technology (IKT). IEEE (2021) – reference: RamachandranMA hybrid grasshopper optimization algorithm and harris hawks optimizer for combined heat and power economic dispatch problemEng. Appl. Artif. Intell.2022111 – volume: 7 start-page: 31243 year: 2019 ident: 7136_CR74 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2902306 – ident: 7136_CR48 doi: 10.1109/IKT54664.2021.9685429 – volume: 104 year: 2021 ident: 7136_CR12 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107197 – start-page: 691 volume-title: Metaheuristics in Machine Learning: Theory and Applications year: 2021 ident: 7136_CR24 doi: 10.1007/978-3-030-70542-8_28 – start-page: 153 volume-title: Metaheuristics and Optimization in Computer and Electrical Engineering year: 2021 ident: 7136_CR28 doi: 10.1007/978-3-030-56689-0_8 – volume: 99 start-page: 56 year: 2018 ident: 7136_CR11 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.01.019 – volume: 177 start-page: 147 year: 2016 ident: 7136_CR79 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.11.018 – ident: 7136_CR13 – volume: 65 start-page: 44 issue: 1 year: 2014 ident: 7136_CR31 publication-title: J. Electr. Eng. – volume: 7 start-page: 38100 year: 2019 ident: 7136_CR84 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2905961 – ident: 7136_CR2 doi: 10.5772/652 – ident: 7136_CR25 doi: 10.1109/ICCPCT.2016.7530150 – ident: 7136_CR6 – ident: 7136_CR14 doi: 10.4018/978-1-5225-2229-4.ch018 – volume: 24 start-page: 14885 issue: 19 year: 2020 ident: 7136_CR71 publication-title: Soft. Comput. doi: 10.1007/s00500-020-04842-7 – volume: 23 start-page: 6023 issue: 15 year: 2019 ident: 7136_CR80 publication-title: Soft. Comput. doi: 10.1007/s00500-018-3586-y – ident: 7136_CR55 doi: 10.31814/stce.huce(nuce)2022-16(2)-03 – volume: 133 year: 2020 ident: 7136_CR60 publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2019.106656 – start-page: 363 volume-title: Artificial Intelligence for COVID-19 year: 2021 ident: 7136_CR16 doi: 10.1007/978-3-030-69744-0_21 – ident: 7136_CR29 – ident: 7136_CR64 doi: 10.1007/s12652-022-03724-0 – ident: 7136_CR10 doi: 10.1016/j.asoc.2020.106683 – ident: 7136_CR46 doi: 10.1007/s13369-022-06605-y – volume: 111 year: 2022 ident: 7136_CR63 publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2022.104753 – volume: 192 year: 2022 ident: 7136_CR65 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116432 – volume: 38 start-page: 727 year: 2016 ident: 7136_CR34 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.10.041 – volume: 24 start-page: 4997 issue: 12 year: 2018 ident: 7136_CR32 publication-title: Microsyst. Technol. doi: 10.1007/s00542-018-3920-4 – ident: 7136_CR81 doi: 10.1109/ISMSIT.2019.8932907 – volume: 24 start-page: 77 issue: 11 year: 2017 ident: 7136_CR83 publication-title: Wulfenia J – volume: 10 start-page: 2834 issue: 22 year: 2021 ident: 7136_CR49 publication-title: Electronics doi: 10.3390/electronics10222834 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 7136_CR35 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.585893 – volume: 79 start-page: 112 year: 2017 ident: 7136_CR73 publication-title: Exp. Syst. Appl. doi: 10.1016/j.eswa.2017.02.035 – ident: 7136_CR82 doi: 10.1145/2001858.2002123 – volume: 59 start-page: 596 year: 2017 ident: 7136_CR8 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.06.033 – ident: 7136_CR77 doi: 10.1109/SIS.2007.367959 – volume: 37 start-page: 3741 issue: 4 year: 2021 ident: 7136_CR57 publication-title: Eng. Comput. doi: 10.1007/s00366-020-01028-5 – ident: 7136_CR4 doi: 10.1002/9780470496916 – ident: 7136_CR3 doi: 10.1115/1.4019264 – volume: 61 start-page: 354 year: 2016 ident: 7136_CR20 publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2016.03.049 – volume: 9 start-page: 1155 issue: 7 year: 2021 ident: 7136_CR38 publication-title: Processes doi: 10.3390/pr9071155 – volume: 97 start-page: 849 year: 2019 ident: 7136_CR56 publication-title: Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2019.02.028 – ident: 7136_CR15 doi: 10.1007/978-3-319-74690-6_11 – volume: 19 start-page: 384 issue: 2 year: 2004 ident: 7136_CR23 publication-title: IEEE Trans. Energy Convers. doi: 10.1109/TEC.2003.821821 – volume: 238 start-page: 106 year: 2014 ident: 7136_CR67 publication-title: Appl. Math. Comput. – volume: 10 start-page: 287 issue: 2 year: 2019 ident: 7136_CR30 publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2018.07.005 – volume: 376 year: 2021 ident: 7136_CR36 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2020.113609 – volume: 191 start-page: 328 year: 2022 ident: 7136_CR22 publication-title: Math. Comput. Simul. doi: 10.1016/j.matcom.2021.08.016 – ident: 7136_CR47 doi: 10.1109/UPEC50034.2021.9548204 – ident: 7136_CR54 doi: 10.1007/s12065-022-00711-4 – volume: 188 year: 2019 ident: 7136_CR72 publication-title: Energy doi: 10.1016/j.energy.2019.116025 – volume: 16 issue: 8 year: 2021 ident: 7136_CR52 publication-title: PLoS ONE doi: 10.1371/journal.pone.0255703 – volume: 492 start-page: 181 year: 2019 ident: 7136_CR76 publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.04.022 – volume: 9 start-page: 84263 year: 2021 ident: 7136_CR37 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3085529 – volume: 141 year: 2020 ident: 7136_CR78 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.112976 – volume: 96 start-page: 120 year: 2016 ident: 7136_CR5 publication-title: Knowl.Based Syst. doi: 10.1016/j.knosys.2015.12.022 – start-page: 413 volume-title: Machine Learning Paradigms: Theory and Application year: 2019 ident: 7136_CR19 doi: 10.1007/978-3-030-02357-7_20 – ident: 7136_CR44 – volume: 25 start-page: 2911 issue: 4 year: 2021 ident: 7136_CR68 publication-title: Soft. Comput. doi: 10.1007/s00500-020-05349-x – volume: 223 year: 2020 ident: 7136_CR62 publication-title: Energy Convers. Manage. doi: 10.1016/j.enconman.2020.113211 – volume: 11 start-page: 1421 issue: 12 year: 2019 ident: 7136_CR75 publication-title: Remote sensing doi: 10.3390/rs11121421 – volume: 189 year: 2022 ident: 7136_CR17 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116063 – volume: 145 year: 2020 ident: 7136_CR69 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.113103 – ident: 7136_CR43 doi: 10.1007/s10489-021-03125-4 – volume: 19 start-page: 3590 issue: 16 year: 2019 ident: 7136_CR61 publication-title: Sensors doi: 10.3390/s19163590 – volume: 89 start-page: 228 year: 2015 ident: 7136_CR9 publication-title: Knowl.Based Syst. doi: 10.1016/j.knosys.2015.07.006 – volume: 244 year: 2020 ident: 7136_CR58 publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2019.118778 – ident: 7136_CR18 doi: 10.1109/TMAG.2011.2176108 – volume: 25 start-page: 4223 issue: 5 year: 2017 ident: 7136_CR26 publication-title: Turk. J. Electr. Eng. Comput. Sci. doi: 10.3906/elk-1612-277 – ident: 7136_CR40 doi: 10.1109/MPS52805.2021.9492572 – ident: 7136_CR45 doi: 10.1155/2022/3987494 – volume: 77 start-page: 418 year: 2016 ident: 7136_CR33 publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2015.11.010 – volume: 51 start-page: 104343 year: 2022 ident: 7136_CR42 publication-title: J. Energy Storage doi: 10.1016/j.est.2022.104343 – volume: 32 start-page: 72 year: 2015 ident: 7136_CR7 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.03.035 – ident: 7136_CR1 – ident: 7136_CR41 doi: 10.1155/2021/9114113 – volume: 9 start-page: 2321 issue: 18 year: 2021 ident: 7136_CR51 publication-title: Mathematics doi: 10.3390/math9182321 – ident: 7136_CR66 – volume: 10 start-page: 16188 year: 2022 ident: 7136_CR39 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3146374 – volume: 24 start-page: 331 issue: 2 year: 2021 ident: 7136_CR27 publication-title: Eng Sci Technol Int J – volume: 46 start-page: 590 issue: 3 year: 2017 ident: 7136_CR70 publication-title: Appl. Intell. doi: 10.1007/s10489-016-0848-1 – volume: 9 start-page: 1774 issue: 10 year: 2021 ident: 7136_CR50 publication-title: Processes doi: 10.3390/pr9101774 – ident: 7136_CR53 doi: 10.1007/s00500-022-06873-8 – volume: 7 start-page: 76529 year: 2019 ident: 7136_CR59 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2921545 – ident: 7136_CR21 doi: 10.1007/978-3-030-04792-4_8 |
SSID | ssib048395113 ssj0001916267 ssj0061873 |
Score | 2.4403768 |
Snippet | The Proportional-Integral-Derivative (PID) controller is a key component in most engineering applications. The main disadvantage of PID is the selection of the... |
SourceID | pubmedcentral proquest pubmed crossref springer |
SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 2191 |
SubjectTerms | Algorithms Arithmetic Computer Engineering and Computer Science Controllers D C motors Electric motors Engineering Humanities and Social Sciences Lower bounds Mathematical analysis multidisciplinary Mutation Optimization Optimization algorithms Parameter estimation Perturbation Proportional integral derivative Research Article-Computer Engineering and Computer Science Science |
Title | Enhanced Arithmetic Optimization Algorithm for Parameter Estimation of PID Controller |
URI | https://link.springer.com/article/10.1007/s13369-022-07136-2 https://www.ncbi.nlm.nih.gov/pubmed/36042895 https://www.proquest.com/docview/2774560122 https://pubmed.ncbi.nlm.nih.gov/PMC9411853 |
Volume | 48 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT8IwFG-MXPRg_HaKpAdv2riPdluPgCCaiBwk2W1Zt1ZIYBjA_9_XboCAmnhZlvStW95r-35v7wuhG5mGKhOhIlQIm1CV2kToyoiSMs6kVIDRTYBs1-_06XPEorJMjs6F2fDf38_AhvI50THn2p7yCRy3FQY3xjHrNxdrh4KiB-zgrf6vAO5xTQNZ2JMeYX4QlTkzP0-7rpe2wOZ2zOSG49Too_YhOiiBJK4Xkj9COzI_RvvfygueoH4rHxgHP1AN54OxzlfEr3BGjMvkS1wfvU_MEAbsinuJjtQCRuMWbPwipxFPFO49PeBmEdI-ktNT1G-33podUrZRICkN6JzwNAt9DqYdWF6ZSrIkECxR0gn0bhcgDye1uUhD5qogzATP_ARsPlDbngpYIB3vDO3mk1xeIEylI3XyLYiXU5a6iWQssd0k84wqdCzkLJgYp2WNcd3qYhSvqiNrxsfA-NgwPnYtdLt85qOosPEndXUhm7jcbbPYBQyrLUsXhs8LMS2n8nxtFXJmoWBNgEsCXV97fSQfDkydbU4djWYsdLcQ9eqVv3_h5f_Ir9Ce7mBfBIJX0e58-imvAefMRQ1V6u1Go1szCx2uj5ED127v5QtJWPTV |
linkProvider | Springer Nature |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fT8IwEG6MPqgPxt-iqH3wTRvZ1q7rI0EIKCIPkPC2rFsrJDAM4P_vtRsgoCY-99Ytd73ed-vdV4TuVBzoRAaaUClLhOq4RKRhRlSUCaaUBoxuC2Rbfr1Ln3usl9PkmF6YtfP7xynkUL4gpubc5FM-ge12hzqCmxVc8SvztUMh0AN28Jb_VwD3uPYCWfBJjzCf9_KemZ-nXY1LG2Bzs2Zy7eDUxqPaITrIgSQuZ5Y_QlsqPUb73-gFT1C3mvbtAT9IDWb9kelXxG-wR4zy5ktcHr6P7RAG7IrbkanUAkXjKjh-1tOIxxq3G0-4kpW0D9XkFHVr1U6lTvJrFEhMOZ0RESeBLyC1g8wr0VEScckirRxuvF2CPZy4JGQcMFfzIJEi8SPI-SBse5ozrhzvDG2n41RdIEyVo0zzLZhXUBa7kWIsKrlR4tlQ6BSQM1diGOcc4-aqi2G4ZEc2ig9B8aFVfOgW0P3imY-MYeNP6eLcNmHubdPQBQxrMksXhs8zMy2m8nyTFQpWQHzFgAsBw6-9OpIO-pZnW1DHoJkCepibevnK37_w8n_it2i33nlths1G6-UK7Znb7LOi8CLank0-1TVgnpm8sYv9C-CN9FI |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDI4QSAgOiDfjmQM3iFjbpG2OaA8xQGMHJu1WNW3CkEY3jfL_sdN2YwyQOMdNKzuxP9cvQi51EppUhYZxpeqMm6TOFHZG1FxIobUBjG4TZLv-XZ_fD8TgSxW_zXavQpJFTQN2acrym0lqbuaFb57nS4aZ6Ohl-QyU8BpH04fhWr9RnSgO5h8QhTf_6wJoyLVjZeGmekz4waCspPl520VrtQRBlzMpv4VTrZVqb5OtEl7S2-I87JAVne2SzS9NB_dIv5UNbdgfqF7z4RtWMdIn0BxvZUkmvR29jO0SBURLezHmbwH7aQvUQVHpSMeG9jpN2igS3Ud6uk_67dZz446VwxVYwgOeM5mkoS_B4QN_LDVxGgdKxEY7AeoABVJykrpUSShcE4SpkqkfgycIxtwzgQi04x2Q1Wyc6SNCuXY0luSC0CUXiRtrIeK6G6eeNZBOjTgVE6Ok7DyOAzBG0bxnMjI-AsZHlvGRWyNXs2cmRd-NP6lPK9lE5R18j1xAtuhvurB8WIhptpXno68oRY0ECwKcEWDX7cWV7HVou29L7iDGqZHrStTzV_7-hcf_I78g671mO3rsdB9OyAaOuC8yxU_Jaj790GcAhHJ1bs_6J3Ci_JA |
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=Enhanced+Arithmetic+Optimization+Algorithm+for+Parameter+Estimation+of+PID+Controller&rft.jtitle=The+Arabian+Journal+for+Science+and+Engineering.+Section+B%2C+Engineering&rft.au=Issa%2C+Mohamed&rft.date=2023-02-01&rft.pub=Springer+Nature+B.V&rft.issn=1319-8025&rft.eissn=2191-4281&rft.volume=48&rft.issue=2&rft.spage=2191&rft.epage=2205&rft_id=info:doi/10.1007%2Fs13369-022-07136-2&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2193-567X&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2193-567X&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2193-567X&client=summon |