Enhanced whale optimization algorithm-based modeling and simulation analysis for industrial system parameter identification
Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial windin...
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| Vydáno v: | The Journal of supercomputing Ročník 79; číslo 13; s. 14489 - 14544 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.09.2023
Springer Nature B.V |
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model, (2) amendments to the humpback whales’ movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems. |
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| AbstractList | Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model, (2) amendments to the humpback whales’ movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems. |
| Author | Al-Betar, Mohammed Azmi Braik, Malik Awadallah, Mohammed Al-Hiary, Heba |
| Author_xml | – sequence: 1 givenname: Malik surname: Braik fullname: Braik, Malik email: mbraik@bau.edu.jo organization: Department of Computer Science, Al-Balqa Applied University – sequence: 2 givenname: Mohammed surname: Awadallah fullname: Awadallah, Mohammed organization: Department of Computer Science, Al-Aqsa University, Artificial Intelligence Research Center (AIRC), Ajman University – sequence: 3 givenname: Mohammed Azmi surname: Al-Betar fullname: Al-Betar, Mohammed Azmi organization: Artificial Intelligence Research Center (AIRC), Ajman University, Department of Information Technology, Al-Balqa Applied University – sequence: 4 givenname: Heba surname: Al-Hiary fullname: Al-Hiary, Heba organization: Department of Computer Information Systems, Al-Balqa Applied University |
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| CitedBy_id | crossref_primary_10_1007_s10586_024_04950_1 crossref_primary_10_1371_journal_pone_0311602 crossref_primary_10_1007_s10462_023_10680_4 crossref_primary_10_1007_s00521_024_10806_x |
| Cites_doi | 10.1007/s10489-016-0767-1 10.1631/jzus.C11a0278 10.1016/j.knosys.2015.12.022 10.1007/s11063-021-10530-w 10.1007/s40092-014-0094-2 10.1007/s10845-022-02009-9 10.1023/A:1021251113462 10.1016/S0967-0661(98)00069-0 10.1016/j.array.2022.100173 10.1007/s10845-021-01900-1 10.1007/s40092-018-0295-1 10.1016/j.advengsoft.2017.01.004 10.1007/s10586-022-03920-9 10.1016/j.chemolab.2020.103932 10.1016/j.advengsoft.2017.07.002 10.1109/TNNLS.2016.2542866 10.1016/j.ins.2014.01.038 10.1007/s12652-019-01373-4 10.1007/978-1-4471-4866-1_13 10.1016/B978-0-323-91781-0.00005-3 10.1007/s40092-017-0183-0 10.1016/j.knosys.2015.07.006 10.32604/cmc.2022.020449 10.1007/s10845-015-1143-4 10.1007/s40092-017-0222-x 10.1080/03610918.2014.931971 10.1007/s00521-020-05145-6 10.1007/s10586-022-03708-x 10.1016/j.eswa.2017.10.028 10.1007/s10462-022-10164-x 10.1016/j.patrec.2008.08.001 10.1007/s00170-019-03621-5 10.1007/978-1-84882-653-3 10.1016/j.scs.2018.05.050 10.1016/j.advengsoft.2016.01.008 10.1007/s00500-017-2909-8 10.1016/j.swevo.2019.03.004 10.1002/cpe.4962 10.1155/2015/931256 10.1007/s12652-020-02623-6 10.18201/ijisae.2019252786 10.1016/j.knosys.2022.108457 10.1080/01621459.1937.10503522 10.1007/s00500-020-05464-9 10.1109/CEC.2000.870323 10.1016/j.neucom.2019.04.086 10.1109/ICNN.1995.488968 10.1016/j.est.2022.104092 |
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| Keywords | Industrial winding process Whale optimization algorithm Artificial neural network Multilayer Perceptron Classification |
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| References | GharehchopoghFSGholizadehHA comprehensive survey: whale optimization algorithm and its applicationsSwarm Evol Comput201948124 ZinggDWNemecMPulliamTHA comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimizationEur J Comput Mech2008171–21031261292.76062 BraikMHammouriAAtwanJAl-BetarMAAwadallahMAWhite shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problemsKnowl-Based Syst2022243 SadatiNChinnamRBNezhadMZObservational data-driven modeling and optimization of manufacturing processesExpert Syst Appl201893456464 Kennedy J, Eberhart R (1995) Particle swarm optimization (pso). In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp 1942–1948 FriedmanMThe use of ranks to avoid the assumption of normality implicit in the analysis of varianceJ Am Stat Assoc19373220067570163.1098.02 FarisHAljarahIMirjaliliSTraining feedforward neural networks using multi-verse optimizer for binary classification problemsAppl Intell2016452322332 Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat, pp 65–70 ZhangLGaoTCaiGHaiKLResearch on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithmJ Energy Storage202249 Refonaa J, Huy DTN, Trung ND, Van Thuc H, Raj R, Haq MA, Kumar A (2022) Probabilistic methods and neural networks in structural engineering. Int J Adv Manuf Technol 1–9 HoY-CPepyneDLSimple explanation of the no-free-lunch theorem and its implicationsJ Optim Theory Appl2002115354957019635681031.91018 SaritasMMYasarAPerformance analysis of ann and naive bayes classification algorithm for data classificationInt J Intell Syst Appl Eng2019728891 TorresPJRMercadoESRifónLAProbabilistic boolean network modeling of an industrial machineJ Intell Manuf2018294875890 CanoJ-RGarcíaSHerreraFSubgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classesPattern Recogn Lett2008291621562164 Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N (2021) Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Humaniz Comput 1–9 Awadallah MA, Abu-Doush I, Al-Betar MA, Braik MS (2023) Metaheuristics for optimizing weights in neural networks. In: Comprehensive Metaheuristics. Elsevier, pp 359–377 MirjaliliSGandomiAHMirjaliliSZSaremiSFarisHMirjaliliSMSalp swarm algorithm: a bio-inspired optimizer for engineering design problemsAdv Eng Softw2017114163191 MirjaliliSSca: a sine cosine algorithm for solving optimization problemsKnowl Based Syst201696120133 BacaninNAlhazmiKZivkovicMVenkatachalamKBezdanTNebhenJTraining multi-layer perceptron with enhanced brain storm optimization metaheuristicsComput Materials Contin202270241994215 Al-Betar MA, Awadallah MA, Doush IA, Alomari OA, Abasi AK, Makhadmeh SN, Alyasseri ZAA (2022) Boosting the training of neural networks through hybrid metaheuristics. Cluster Comput 1–23 BastogneTNouraHSibillePRichardAMultivariable identification of a winding process by subspace methods for tension controlControl Eng Pract19986910771088 ArunkumarNMohammedMAMostafaSAIbrahimDARodriguesJJde AlbuquerqueVHCFully automatic model-based segmentation and classification approach for mri brain tumor using artificial neural networksConcurr Comput Pract Exp2020321 BraikMAl-ZoubiHAl-HiaryHArtificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case studySoft Comput20212545454569 Noura H, Theilliol D, Ponsart J-C, Chamseddine A (2009) Fault-tolerant control systems: design and practical applications. Springer Science & Business Media MirjaliliSMoth-flame optimization algorithm: a novel nature-inspired heuristic paradigmKnowl-Based Syst201589228249 KhosraviAKouryRMachadoLPabonJPrediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference systemSustain Energy Technol Assess201825146160 ChangP-CWuJ-LXuYZhangMLuX-YBike sharing demand prediction using artificial immune system and artificial neural networkSoft Comput2019232613626 Al-Azzeh J, Alqadi Z, Abuzalata M (2019) Performance analysis of artificial neural networks used for color image recognition and retrieving. Int J Comput Sci Mobile Comput 8(2) MirjaliliSLewisAThe whale optimization algorithmAdv Eng Softw2016955167 MosaviAShirzadiAChoubinBTaromidehFHosseiniFSBorjiMShahabiHSalvatiADinevaAATowards an ensemble machine learning model of random subspace based functional tree classifier for snow avalanche susceptibility mappingIEEE Access20208145968 TianZGanWZouXZhangYGaoWPerformance prediction of a cryogenic organic rankine cycle based on back propagation neural network optimized by genetic algorithmEnergy2022254 BraikMShetaAAl-HiaryHA novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithmNeural Comput Appl20213325152547 Braik M (2022) Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images. Cluster Comput 1–37 NikabadiMNaderiRA hybrid algorithm for unrelated parallel machines schedulingInt J Ind Eng Comput201674681702 Owusu P (2020) Microstrip butterworth lowpass filter design YeZKimMKPredicting electricity consumption in a building using an optimized back-propagation and levenberg-marquardt back-propagation neural network: Case study of a shopping mall in chinaSustain Cities Soc201842176183 KhanAShahRImranMKhanABangashJIShahKAn alternative approach to neural network training based on hybrid bio meta-heuristic algorithmJ Ambient Intell Humaniz Comput2019101038213830 SaremiSMirjaliliSLewisAGrasshopper optimisation algorithm: theory and applicationAdv Eng Softw20171053047 NozariHABanadakiHDMokhtareMVahedSHIntelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networksJ Zhejiang Univ SCIENCE C2012136403412 Wang Y, Li H, Qi C (2020) An adaptive mode convolutional neural network based on bar-shaped structures and its operation modeling to complex industrial processes. Chemom Intell Lab Syst, p 103932 Wang Z, Rosen D (2022) Manufacturing process classification based on heat kernel signature and convolutional neural networks. J Intell Manuf 1–23 Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015 DaoSDAbharyKMarianROptimisation of assembly scheduling in vcim systems using genetic algorithmJ Ind Eng Int2017133275296 BraikMAl-ZoubiHRyalatMShetaAAlzubiOMemory based hybrid crow search algorithm for solving numerical and constrained global optimization problemsArtif Intell Rev20235612799 MoslemipourGA hybrid cs-sa intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demandsJ Ind Eng Int2018142429442 RojasMGOliveraACVidalPJOptimising multilayer perceptron weights and biases through a cellular genetic algorithm for medical data classificationArray202214 YıldızARHybrid taguchi-harmony search algorithm for solving engineering optimization problemsInt J Ind Eng2008153286293 Hussian A, Sheta A, Kamel M, Telbaney M, Abdelwahab A (2000) Modeling of a winding machine using genetic programming. In: Evolutionary Computation, Proceedings of the 2000 Congress on IEEE 1:398–402 Braik M, Sheta A, Al-Hiary H, Aljahdali S (2022) Enhanced cuckoo search algorithm for industrial winding process modeling. J Intell Manuf 1–30 XuFPunC-MLiHZhangYSongYGaoHTraining feed-forward artificial neural networks with a modified artificial bee colony algorithmNeurocomputing20204166984 Sheta A F, Braik M, Öznergiz E, Ayesh A, Masud M (2013) Design and automation for manufacturing processes: An intelligent business modeling using adaptive neuro-fuzzy inference systems. Bus Intell Perform Manag, Springer, pp 191–208 PereiraDGAfonsoAMedeirosFMOverview of friedman’s test and post-hoc analysisCommun Stat Simul Comput201544102636265333661931474.62288 DixitSRDasSRDhupalDParametric optimization of nd: Yag laser microgrooving on aluminum oxide using integrated rsm-ann-ga approachJ Ind Eng Int2019152333349 Wdaa ASI, Sttar A (2008) Differential evolution for neural networks learning enhancement. In: Ph.D. Dissertation, Universiti Teknologi Malaysia Johor Bahru AyoughAKhorshidvandBDesigning a manufacturing cell system by assigning workforceJ Ind Eng Manag20191211326 SunKHuangS-HWongDS-HJangS-SDesign and application of a variable selection method for multilayer perceptron neural network with lassoIEEE Trans Neural Netw Learn Syst201628613861396 BraikMA hybrid multi-gene genetic programming with capuchin search algorithm for modeling a nonlinear challenge problem: Modeling industrial winding process, case studyNeural Process Lett202153428732916 MousaviSHNazemiAHafezalkotobAUsing and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generatorsJ Ind Eng Int20151115972 MirjaliliSMirjaliliSMLewisALet a biogeography-based optimizer train your multi-layer perceptronInf Sci20142691882093180809 Sheta A, Braik M, Al-Hiary H (2019) Modeling the tennessee eastman chemical process reactor using bio-inspired feedforward neural network (bi-ff-nn). Int J Adv Manuf Technol 1–22 AR Yıldız (5215_CR43) 2008; 15 PJR Torres (5215_CR33) 2018; 29 SR Dixit (5215_CR40) 2019; 15 M Braik (5215_CR9) 2023; 56 A Khosravi (5215_CR28) 2018; 25 5215_CR30 5215_CR34 S Mirjalili (5215_CR54) 2015; 89 H Faris (5215_CR8) 2016; 45 S Mirjalili (5215_CR55) 2016; 96 S Mirjalili (5215_CR49) 2014; 269 5215_CR5 G Moslemipour (5215_CR42) 2018; 14 SD Dao (5215_CR41) 2017; 13 N Sadati (5215_CR32) 2018; 93 5215_CR4 5215_CR1 M Friedman (5215_CR51) 1937; 32 5215_CR47 5215_CR48 A Ayough (5215_CR38) 2019; 12 M Braik (5215_CR18) 2021; 25 MM Saritas (5215_CR6) 2019; 7 J-R Cano (5215_CR53) 2008; 29 5215_CR52 Y-C Ho (5215_CR21) 2002; 115 DG Pereira (5215_CR60) 2015; 44 F Xu (5215_CR16) 2020; 416 S Saremi (5215_CR50) 2017; 105 5215_CR14 5215_CR58 P-C Chang (5215_CR3) 2019; 23 5215_CR15 Z Ye (5215_CR35) 2018; 42 5215_CR19 FS Gharehchopogh (5215_CR23) 2019; 48 M Nikabadi (5215_CR37) 2016; 7 MG Rojas (5215_CR12) 2022; 14 S Mirjalili (5215_CR56) 2017; 114 A Mosavi (5215_CR46) 2020; 8 Z Tian (5215_CR13) 2022; 254 5215_CR20 M Braik (5215_CR11) 2021; 53 5215_CR24 M Braik (5215_CR59) 2022; 243 K Sun (5215_CR44) 2016; 28 5215_CR27 DW Zingg (5215_CR36) 2008; 17 A Khan (5215_CR17) 2019; 10 S Mirjalili (5215_CR22) 2016; 95 5215_CR25 M Braik (5215_CR57) 2021; 33 N Bacanin (5215_CR10) 2022; 70 5215_CR26 L Zhang (5215_CR7) 2022; 49 5215_CR29 T Bastogne (5215_CR45) 1998; 6 HA Nozari (5215_CR31) 2012; 13 SH Mousavi (5215_CR39) 2015; 11 N Arunkumar (5215_CR2) 2020; 32 |
| References_xml | – reference: Kennedy J, Eberhart R (1995) Particle swarm optimization (pso). In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp 1942–1948 – reference: SaremiSMirjaliliSLewisAGrasshopper optimisation algorithm: theory and applicationAdv Eng Softw20171053047 – reference: Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015 – reference: CanoJ-RGarcíaSHerreraFSubgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classesPattern Recogn Lett2008291621562164 – reference: MirjaliliSGandomiAHMirjaliliSZSaremiSFarisHMirjaliliSMSalp swarm algorithm: a bio-inspired optimizer for engineering design problemsAdv Eng Softw2017114163191 – reference: HoY-CPepyneDLSimple explanation of the no-free-lunch theorem and its implicationsJ Optim Theory Appl2002115354957019635681031.91018 – reference: Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N (2021) Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Humaniz Comput 1–9 – reference: TorresPJRMercadoESRifónLAProbabilistic boolean network modeling of an industrial machineJ Intell Manuf2018294875890 – reference: KhanAShahRImranMKhanABangashJIShahKAn alternative approach to neural network training based on hybrid bio meta-heuristic algorithmJ Ambient Intell Humaniz Comput2019101038213830 – reference: ArunkumarNMohammedMAMostafaSAIbrahimDARodriguesJJde AlbuquerqueVHCFully automatic model-based segmentation and classification approach for mri brain tumor using artificial neural networksConcurr Comput Pract Exp2020321 – reference: Sheta A, Braik M, Al-Hiary H (2019) Modeling the tennessee eastman chemical process reactor using bio-inspired feedforward neural network (bi-ff-nn). Int J Adv Manuf Technol 1–22 – reference: Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat, pp 65–70 – reference: MirjaliliSMoth-flame optimization algorithm: a novel nature-inspired heuristic paradigmKnowl-Based Syst201589228249 – reference: PereiraDGAfonsoAMedeirosFMOverview of friedman’s test and post-hoc analysisCommun Stat Simul Comput201544102636265333661931474.62288 – reference: ZhangLGaoTCaiGHaiKLResearch on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithmJ Energy Storage202249 – reference: Al-Betar MA, Awadallah MA, Doush IA, Alomari OA, Abasi AK, Makhadmeh SN, Alyasseri ZAA (2022) Boosting the training of neural networks through hybrid metaheuristics. Cluster Comput 1–23 – reference: BraikMAl-ZoubiHAl-HiaryHArtificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case studySoft Comput20212545454569 – reference: MirjaliliSSca: a sine cosine algorithm for solving optimization problemsKnowl Based Syst201696120133 – reference: Braik M (2022) Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images. Cluster Comput 1–37 – reference: SunKHuangS-HWongDS-HJangS-SDesign and application of a variable selection method for multilayer perceptron neural network with lassoIEEE Trans Neural Netw Learn Syst201628613861396 – reference: BraikMHammouriAAtwanJAl-BetarMAAwadallahMAWhite shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problemsKnowl-Based Syst2022243 – reference: FriedmanMThe use of ranks to avoid the assumption of normality implicit in the analysis of varianceJ Am Stat Assoc19373220067570163.1098.02 – reference: MirjaliliSLewisAThe whale optimization algorithmAdv Eng Softw2016955167 – reference: MousaviSHNazemiAHafezalkotobAUsing and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generatorsJ Ind Eng Int20151115972 – reference: SaritasMMYasarAPerformance analysis of ann and naive bayes classification algorithm for data classificationInt J Intell Syst Appl Eng2019728891 – reference: FarisHAljarahIMirjaliliSTraining feedforward neural networks using multi-verse optimizer for binary classification problemsAppl Intell2016452322332 – reference: DixitSRDasSRDhupalDParametric optimization of nd: Yag laser microgrooving on aluminum oxide using integrated rsm-ann-ga approachJ Ind Eng Int2019152333349 – reference: MoslemipourGA hybrid cs-sa intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demandsJ Ind Eng Int2018142429442 – reference: NozariHABanadakiHDMokhtareMVahedSHIntelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networksJ Zhejiang Univ SCIENCE C2012136403412 – reference: BraikMA hybrid multi-gene genetic programming with capuchin search algorithm for modeling a nonlinear challenge problem: Modeling industrial winding process, case studyNeural Process Lett202153428732916 – reference: Refonaa J, Huy DTN, Trung ND, Van Thuc H, Raj R, Haq MA, Kumar A (2022) Probabilistic methods and neural networks in structural engineering. Int J Adv Manuf Technol 1–9 – reference: ZinggDWNemecMPulliamTHA comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimizationEur J Comput Mech2008171–21031261292.76062 – reference: Al-Azzeh J, Alqadi Z, Abuzalata M (2019) Performance analysis of artificial neural networks used for color image recognition and retrieving. Int J Comput Sci Mobile Comput 8(2) – reference: Hussian A, Sheta A, Kamel M, Telbaney M, Abdelwahab A (2000) Modeling of a winding machine using genetic programming. In: Evolutionary Computation, Proceedings of the 2000 Congress on IEEE 1:398–402 – reference: Wang Y, Li H, Qi C (2020) An adaptive mode convolutional neural network based on bar-shaped structures and its operation modeling to complex industrial processes. Chemom Intell Lab Syst, p 103932 – reference: YeZKimMKPredicting electricity consumption in a building using an optimized back-propagation and levenberg-marquardt back-propagation neural network: Case study of a shopping mall in chinaSustain Cities Soc201842176183 – reference: Noura H, Theilliol D, Ponsart J-C, Chamseddine A (2009) Fault-tolerant control systems: design and practical applications. Springer Science & Business Media – reference: SadatiNChinnamRBNezhadMZObservational data-driven modeling and optimization of manufacturing processesExpert Syst Appl201893456464 – reference: BastogneTNouraHSibillePRichardAMultivariable identification of a winding process by subspace methods for tension controlControl Eng Pract19986910771088 – reference: KhosraviAKouryRMachadoLPabonJPrediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference systemSustain Energy Technol Assess201825146160 – reference: Wdaa ASI, Sttar A (2008) Differential evolution for neural networks learning enhancement. In: Ph.D. Dissertation, Universiti Teknologi Malaysia Johor Bahru – reference: TianZGanWZouXZhangYGaoWPerformance prediction of a cryogenic organic rankine cycle based on back propagation neural network optimized by genetic algorithmEnergy2022254 – reference: Braik M, Sheta A, Al-Hiary H, Aljahdali S (2022) Enhanced cuckoo search algorithm for industrial winding process modeling. J Intell Manuf 1–30 – reference: DaoSDAbharyKMarianROptimisation of assembly scheduling in vcim systems using genetic algorithmJ Ind Eng Int2017133275296 – reference: Sheta A F, Braik M, Öznergiz E, Ayesh A, Masud M (2013) Design and automation for manufacturing processes: An intelligent business modeling using adaptive neuro-fuzzy inference systems. Bus Intell Perform Manag, Springer, pp 191–208 – reference: BraikMShetaAAl-HiaryHA novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithmNeural Comput Appl20213325152547 – reference: AyoughAKhorshidvandBDesigning a manufacturing cell system by assigning workforceJ Ind Eng Manag20191211326 – reference: MosaviAShirzadiAChoubinBTaromidehFHosseiniFSBorjiMShahabiHSalvatiADinevaAATowards an ensemble machine learning model of random subspace based functional tree classifier for snow avalanche susceptibility mappingIEEE Access20208145968 – reference: RojasMGOliveraACVidalPJOptimising multilayer perceptron weights and biases through a cellular genetic algorithm for medical data classificationArray202214 – reference: XuFPunC-MLiHZhangYSongYGaoHTraining feed-forward artificial neural networks with a modified artificial bee colony algorithmNeurocomputing20204166984 – reference: ChangP-CWuJ-LXuYZhangMLuX-YBike sharing demand prediction using artificial immune system and artificial neural networkSoft Comput2019232613626 – reference: GharehchopoghFSGholizadehHA comprehensive survey: whale optimization algorithm and its applicationsSwarm Evol Comput201948124 – reference: MirjaliliSMirjaliliSMLewisALet a biogeography-based optimizer train your multi-layer perceptronInf Sci20142691882093180809 – reference: NikabadiMNaderiRA hybrid algorithm for unrelated parallel machines schedulingInt J Ind Eng Comput201674681702 – reference: BacaninNAlhazmiKZivkovicMVenkatachalamKBezdanTNebhenJTraining multi-layer perceptron with enhanced brain storm optimization metaheuristicsComput Materials Contin202270241994215 – reference: Wang Z, Rosen D (2022) Manufacturing process classification based on heat kernel signature and convolutional neural networks. J Intell Manuf 1–23 – reference: YıldızARHybrid taguchi-harmony search algorithm for solving engineering optimization problemsInt J Ind Eng2008153286293 – reference: BraikMAl-ZoubiHRyalatMShetaAAlzubiOMemory based hybrid crow search algorithm for solving numerical and constrained global optimization problemsArtif Intell Rev20235612799 – reference: Awadallah MA, Abu-Doush I, Al-Betar MA, Braik MS (2023) Metaheuristics for optimizing weights in neural networks. In: Comprehensive Metaheuristics. Elsevier, pp 359–377 – reference: Owusu P (2020) Microstrip butterworth lowpass filter design – volume: 45 start-page: 322 issue: 2 year: 2016 ident: 5215_CR8 publication-title: Appl Intell doi: 10.1007/s10489-016-0767-1 – volume: 13 start-page: 403 issue: 6 year: 2012 ident: 5215_CR31 publication-title: J Zhejiang Univ SCIENCE C doi: 10.1631/jzus.C11a0278 – volume: 96 start-page: 120 year: 2016 ident: 5215_CR55 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2015.12.022 – volume: 53 start-page: 2873 issue: 4 year: 2021 ident: 5215_CR11 publication-title: Neural Process Lett doi: 10.1007/s11063-021-10530-w – volume: 11 start-page: 59 issue: 1 year: 2015 ident: 5215_CR39 publication-title: J Ind Eng Int doi: 10.1007/s40092-014-0094-2 – ident: 5215_CR4 doi: 10.1007/s10845-022-02009-9 – volume: 115 start-page: 549 issue: 3 year: 2002 ident: 5215_CR21 publication-title: J Optim Theory Appl doi: 10.1023/A:1021251113462 – volume: 6 start-page: 1077 issue: 9 year: 1998 ident: 5215_CR45 publication-title: Control Eng Pract doi: 10.1016/S0967-0661(98)00069-0 – volume: 14 year: 2022 ident: 5215_CR12 publication-title: Array doi: 10.1016/j.array.2022.100173 – ident: 5215_CR25 doi: 10.1007/s10845-021-01900-1 – volume: 15 start-page: 333 issue: 2 year: 2019 ident: 5215_CR40 publication-title: J Ind Eng Int doi: 10.1007/s40092-018-0295-1 – volume: 105 start-page: 30 year: 2017 ident: 5215_CR50 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2017.01.004 – ident: 5215_CR52 – ident: 5215_CR24 doi: 10.1007/s10586-022-03920-9 – ident: 5215_CR34 doi: 10.1016/j.chemolab.2020.103932 – ident: 5215_CR47 – volume: 254 year: 2022 ident: 5215_CR13 publication-title: Energy – volume: 114 start-page: 163 year: 2017 ident: 5215_CR56 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2017.07.002 – volume: 15 start-page: 286 issue: 3 year: 2008 ident: 5215_CR43 publication-title: Int J Ind Eng – volume: 28 start-page: 1386 issue: 6 year: 2016 ident: 5215_CR44 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2016.2542866 – volume: 269 start-page: 188 year: 2014 ident: 5215_CR49 publication-title: Inf Sci doi: 10.1016/j.ins.2014.01.038 – volume: 10 start-page: 3821 issue: 10 year: 2019 ident: 5215_CR17 publication-title: J Ambient Intell Humaniz Comput doi: 10.1007/s12652-019-01373-4 – ident: 5215_CR27 doi: 10.1007/978-1-4471-4866-1_13 – ident: 5215_CR20 doi: 10.1016/B978-0-323-91781-0.00005-3 – volume: 13 start-page: 275 issue: 3 year: 2017 ident: 5215_CR41 publication-title: J Ind Eng Int doi: 10.1007/s40092-017-0183-0 – volume: 89 start-page: 228 year: 2015 ident: 5215_CR54 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2015.07.006 – volume: 70 start-page: 4199 issue: 2 year: 2022 ident: 5215_CR10 publication-title: Comput Materials Contin doi: 10.32604/cmc.2022.020449 – volume: 29 start-page: 875 issue: 4 year: 2018 ident: 5215_CR33 publication-title: J Intell Manuf doi: 10.1007/s10845-015-1143-4 – volume: 14 start-page: 429 issue: 2 year: 2018 ident: 5215_CR42 publication-title: J Ind Eng Int doi: 10.1007/s40092-017-0222-x – volume: 44 start-page: 2636 issue: 10 year: 2015 ident: 5215_CR60 publication-title: Commun Stat Simul Comput doi: 10.1080/03610918.2014.931971 – volume: 12 start-page: 13 issue: 1 year: 2019 ident: 5215_CR38 publication-title: J Ind Eng Manag – volume: 33 start-page: 2515 year: 2021 ident: 5215_CR57 publication-title: Neural Comput Appl doi: 10.1007/s00521-020-05145-6 – ident: 5215_CR19 doi: 10.1007/s10586-022-03708-x – volume: 93 start-page: 456 year: 2018 ident: 5215_CR32 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2017.10.028 – ident: 5215_CR48 – volume: 56 start-page: 27 issue: 1 year: 2023 ident: 5215_CR9 publication-title: Artif Intell Rev doi: 10.1007/s10462-022-10164-x – volume: 29 start-page: 2156 issue: 16 year: 2008 ident: 5215_CR53 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2008.08.001 – ident: 5215_CR26 doi: 10.1007/s00170-019-03621-5 – ident: 5215_CR30 doi: 10.1007/978-1-84882-653-3 – volume: 42 start-page: 176 year: 2018 ident: 5215_CR35 publication-title: Sustain Cities Soc doi: 10.1016/j.scs.2018.05.050 – volume: 95 start-page: 51 year: 2016 ident: 5215_CR22 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2016.01.008 – volume: 23 start-page: 613 issue: 2 year: 2019 ident: 5215_CR3 publication-title: Soft Comput doi: 10.1007/s00500-017-2909-8 – volume: 48 start-page: 1 year: 2019 ident: 5215_CR23 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2019.03.004 – volume: 32 issue: 1 year: 2020 ident: 5215_CR2 publication-title: Concurr Comput Pract Exp doi: 10.1002/cpe.4962 – ident: 5215_CR15 doi: 10.1155/2015/931256 – volume: 8 start-page: 145 year: 2020 ident: 5215_CR46 publication-title: IEEE Access – ident: 5215_CR14 doi: 10.1007/s12652-020-02623-6 – volume: 7 start-page: 88 issue: 2 year: 2019 ident: 5215_CR6 publication-title: Int J Intell Syst Appl Eng doi: 10.18201/ijisae.2019252786 – volume: 243 year: 2022 ident: 5215_CR59 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2022.108457 – ident: 5215_CR5 – volume: 7 start-page: 681 issue: 4 year: 2016 ident: 5215_CR37 publication-title: Int J Ind Eng Comput – ident: 5215_CR1 – volume: 32 start-page: 675 issue: 200 year: 1937 ident: 5215_CR51 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1937.10503522 – volume: 25 start-page: 4545 year: 2021 ident: 5215_CR18 publication-title: Soft Comput doi: 10.1007/s00500-020-05464-9 – volume: 17 start-page: 103 issue: 1–2 year: 2008 ident: 5215_CR36 publication-title: Eur J Comput Mech – volume: 25 start-page: 146 year: 2018 ident: 5215_CR28 publication-title: Sustain Energy Technol Assess – ident: 5215_CR29 doi: 10.1109/CEC.2000.870323 – volume: 416 start-page: 69 year: 2020 ident: 5215_CR16 publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.04.086 – ident: 5215_CR58 doi: 10.1109/ICNN.1995.488968 – volume: 49 year: 2022 ident: 5215_CR7 publication-title: J Energy Storage doi: 10.1016/j.est.2022.104092 |
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