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For: Zhang H, Heidari AA, Wang M, Zhang L, Chen H, Li C. Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules. Energy Conversion and Management 2020;211:112764. [DOI: 10.1016/j.enconman.2020.112764] [Cited by in Crossref: 102] [Cited by in F6Publishing: 83] [Article Influence: 34.0] [Reference Citation Analysis]
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18 Ramadan A, Kamel S, Korashy A, Almalaq A, Domínguez-garcía JL. An enhanced Harris Hawk optimization algorithm for parameter estimation of single, double and triple diode photovoltaic models. Soft Comput. [DOI: 10.1007/s00500-022-07109-5] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Xu B, Heidari AA, Kuang F, Zhang S, Chen H, Cai Z. Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells. Intl J of Energy Research. [DOI: 10.1002/er.8011] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
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21 Shan W, Qiao Z, Heidari AA, Gui W, Chen H, Teng Y, Liang Y, Lv T. An efficient rotational direction heap-based optimization with orthogonal structure for medical diagnosis. Comput Biol Med 2022;146:105563. [PMID: 35551010 DOI: 10.1016/j.compbiomed.2022.105563] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Premkumar M, Jangir P, Kumar C, Sundarsingh Jebaseelan SDT, Alhelou HH, Madurai Elavarasan R, Chen H. Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method. IET Renewable Power Gen. [DOI: 10.1049/rpg2.12475] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
23 Rezk H, Abdelkareem MA. Optimal parameter identification of triple diode model for solar photovoltaic panel and cells. Energy Reports 2022;8:1179-88. [DOI: 10.1016/j.egyr.2021.11.179] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
24 Yousri D, Shaker Y, Mirjalili S, Allam D. An efficient photovoltaic modeling using an Adaptive Fractional-order Archimedes Optimization Algorithm: Validation with partial shading conditions. Solar Energy 2022;236:26-50. [DOI: 10.1016/j.solener.2021.12.063] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
25 Lin H, Ahmadianfar I, Amiri Golilarz N, Jamei M, Heidari AA, Kuang F, Zhang S, Chen H. Adaptive slime mould algorithm for optimal design of photovoltaic models. Energy Science & Engineering. [DOI: 10.1002/ese3.1115] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
26 Yu S, Chen Z, Heidari AA, Zhou W, Chen H, Xiao L. Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer. IET Renewable Power Gen. [DOI: 10.1049/rpg2.12451] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
27 Xu B, Heidari AA, Kuang F, Zhang S, Chen H, Cai Z. Performance optimization of photovoltaic systems: Reassessment of political optimization with a quantum Nelder-mead functionality. Solar Energy 2022;234:39-63. [DOI: 10.1016/j.solener.2022.01.048] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
28 Ridha HM, Hizam H, Mirjalili S, Othman ML, Ya'acob ME, Ahmadipour M, Ismaeel NQ. On the problem formulation for parameter extraction of the photovoltaic model: Novel integration of hybrid evolutionary algorithm and Levenberg Marquardt based on adaptive damping parameter formula. Energy Conversion and Management 2022;256:115403. [DOI: 10.1016/j.enconman.2022.115403] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
29 Yu S, Heidari AA, Liang G, Chen C, Chen H, Shao Q. Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization. Optik 2022;252:168513. [DOI: 10.1016/j.ijleo.2021.168513] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]
30 Haddad S, Lekouaghet B, Benghanem M, Soukkou A, Rabhi A. Parameter Estimation of Solar Modules Operating Under Outdoor Operational Conditions Using Artificial Hummingbird Algorithm. IEEE Access 2022;10:51299-314. [DOI: 10.1109/access.2022.3174222] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
31 Fan Y, Wang P, Heidari AA, Chen H, Hamzaturabieh, Mafarja M. Random reselection particle swarm optimization for optimal design of solar photovoltaic modules. Energy 2022;239:121865. [DOI: 10.1016/j.energy.2021.121865] [Cited by in Crossref: 28] [Cited by in F6Publishing: 28] [Article Influence: 28.0] [Reference Citation Analysis]
32 Nunes HGG, Portugal JPA, Pombo JAN, Mariano SJPS, Calado MRA. Parameter Estimation of Per-Unit Photovoltaic Models Using Optimization Algorithms: Comparative Study. Handbook of Nature-Inspired Optimization Algorithms: The State of the Art 2022. [DOI: 10.1007/978-3-031-07512-4_6] [Reference Citation Analysis]
33 Ridha HM, Hizam H, Mirjalili S, Othman ML, Ya'acob ME, Abualigah L. A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models. IEEE Access 2022;10:11110-37. [DOI: 10.1109/access.2022.3142779] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
34 Kotb MF, El-fergany AA, Gouda EA, Agwa AM. Dynamic Performance Evaluation of Photovoltaic Three-Diode Model-Based Rung-Kutta Optimizer. IEEE Access 2022;10:38309-23. [DOI: 10.1109/access.2022.3165035] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
35 Farah A, Belazi A, Benabdallah F, Almalaq A, Chtourou M, Abido M. Parameter extraction of photovoltaic models using a comprehensive learning Rao-1 algorithm. Energy Conversion and Management 2022;252:115057. [DOI: 10.1016/j.enconman.2021.115057] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
36 Chen W, Chen Z, Hsu S, Park Y, Juan JC. Reactor design of methanol steam reforming by evolutionary computation and hydrogen production maximization by machine learning. Intl J of Energy Research 2021. [DOI: 10.1002/er.7543] [Reference Citation Analysis]
37 Wang X, Chu S, Snášel V, Kong L, Pan J, Shehadeh HA. A two-phase quasi-affine transformation evolution with feedback for parameter identification of photovoltaic models. Applied Soft Computing 2021;113:107978. [DOI: 10.1016/j.asoc.2021.107978] [Reference Citation Analysis]
38 Dong R, Chen H, Heidari AA, Turabieh H, Mafarja M, Wang S. Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem. Knowledge-Based Systems 2021;233:107529. [DOI: 10.1016/j.knosys.2021.107529] [Cited by in Crossref: 19] [Cited by in F6Publishing: 27] [Article Influence: 9.5] [Reference Citation Analysis]
39 Xu Y, Huang H, Heidari AA, Gui W, Ye X, Chen Y, Chen H, Pan Z. MFeature: Towards high performance evolutionary tools for feature selection. Expert Systems with Applications 2021;186:115655. [DOI: 10.1016/j.eswa.2021.115655] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
40 Kumar C, Mary DM. Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method. J Comput Electron 2021;20:2563-93. [DOI: 10.1007/s10825-021-01812-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
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42 Fan Y, Wang P, Heidari AA, Zhao X, Turabieh H, Chen H. Delayed dynamic step shuffling frog-leaping algorithm for optimal design of photovoltaic models. Energy Reports 2021;7:228-46. [DOI: 10.1016/j.egyr.2020.12.013] [Cited by in Crossref: 20] [Cited by in F6Publishing: 21] [Article Influence: 10.0] [Reference Citation Analysis]
43 Weng X, Heidari AA, Liang G, Chen H, Ma X. An evolutionary Nelder–Mead slime mould algorithm with random learning for efficient design of photovoltaic models. Energy Reports 2021;7:8784-804. [DOI: 10.1016/j.egyr.2021.11.019] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 3.5] [Reference Citation Analysis]
44 Rezk H, Babu TS, Al-dhaifallah M, Ziedan HA. A robust parameter estimation approach based on stochastic fractal search optimization algorithm applied to solar PV parameters. Energy Reports 2021;7:620-40. [DOI: 10.1016/j.egyr.2021.01.024] [Cited by in Crossref: 32] [Cited by in F6Publishing: 25] [Article Influence: 16.0] [Reference Citation Analysis]
45 Liu Y, Heidari AA, Ye X, Liang G, Chen H, He C. Boosting slime mould algorithm for parameter identification of photovoltaic models. Energy 2021;234:121164. [DOI: 10.1016/j.energy.2021.121164] [Cited by in Crossref: 29] [Cited by in F6Publishing: 32] [Article Influence: 14.5] [Reference Citation Analysis]
46 Liu Y, Heidari AA, Ye X, Chi C, Zhao X, Ma C, Turabieh H, Chen H, Le R. Evolutionary shuffled frog leaping with memory pool for parameter optimization. Energy Reports 2021;7:584-606. [DOI: 10.1016/j.egyr.2021.01.001] [Cited by in Crossref: 24] [Cited by in F6Publishing: 24] [Article Influence: 12.0] [Reference Citation Analysis]
47 Ahmadianfar I, Gong W, Heidari AA, Golilarz NA, Samadi-koucheksaraee A, Chen H. Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems. Energy Reports 2021;7:3979-97. [DOI: 10.1016/j.egyr.2021.06.064] [Cited by in Crossref: 22] [Cited by in F6Publishing: 24] [Article Influence: 11.0] [Reference Citation Analysis]
48 Zhou W, Wang P, Heidari AA, Zhao X, Turabieh H, Mafarja M, Chen H. Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules. Energy Reports 2021;7:5175-202. [DOI: 10.1016/j.egyr.2021.07.041] [Cited by in Crossref: 17] [Cited by in F6Publishing: 20] [Article Influence: 8.5] [Reference Citation Analysis]
49 El-fergany AA. Parameters identification of PV model using improved slime mould optimizer and Lambert W-function. Energy Reports 2021;7:875-87. [DOI: 10.1016/j.egyr.2021.01.093] [Cited by in Crossref: 38] [Cited by in F6Publishing: 32] [Article Influence: 19.0] [Reference Citation Analysis]
50 Chen C, Wang X, Yu H, Wang M, Chen H. Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms. Mathematics and Computers in Simulation 2021;188:291-318. [DOI: 10.1016/j.matcom.2021.04.006] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 7.5] [Reference Citation Analysis]
51 Wang M, Zhang Q, Chen H, Heidari AA, Mafarja M, Turabieh H. Evaluation of constraint in photovoltaic cells using ensemble multi-strategy shuffled frog leading algorithms. Energy Conversion and Management 2021;244:114484. [DOI: 10.1016/j.enconman.2021.114484] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 9.0] [Reference Citation Analysis]
52 Weng X, Heidari AA, Liang G, Chen H, Ma X, Mafarja M, Turabieh H. Laplacian Nelder-Mead spherical evolution for parameter estimation of photovoltaic models. Energy Conversion and Management 2021;243:114223. [DOI: 10.1016/j.enconman.2021.114223] [Cited by in Crossref: 20] [Cited by in F6Publishing: 18] [Article Influence: 10.0] [Reference Citation Analysis]
53 Chen C, Wang X, Wu C, Mafarja M, Turabieh H, Chen H. Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine. Electronics 2021;10:2115. [DOI: 10.3390/electronics10172115] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
54 Wang G, Gui W, Liang G, Zhao X, Wang M, Mafarja M, Turabieh H, Xin J, Chen H, Ma X, Sun Y. Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks. Complexity 2021;2021:1-33. [DOI: 10.1155/2021/8130378] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
55 Xia J, Zhang H, Li R, Chen H, Turabieh H, Mafarja M, Pan Z. Generalized Oppositional Moth Flame Optimization with Crossover Strategy: An Approach for Medical Diagnosis. J Bionic Eng 2021;18:991-1010. [DOI: 10.1007/s42235-021-0068-1] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
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60 Sohani A, Sayyaadi H, Moradi MH, Nastasi B, Groppi D, Zabihigivi M, Astiaso Garcia D. Comparative study of temperature distribution impact on prediction accuracy of simulation approaches for poly and mono crystalline solar modules. Energy Conversion and Management 2021;239:114221. [DOI: 10.1016/j.enconman.2021.114221] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
61 Dündar A, İzci D, Ekinci S, Eker E. A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization. DÜMF Mühendislik Dergisi 2021. [DOI: 10.24012/dumf.955645] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
62 Gao S, Xiang C, Lee TH. Highly efficient photovoltaic parameter estimation using parallel particle swarm optimization on a GPU. 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) 2021. [DOI: 10.1109/isie45552.2021.9576495] [Reference Citation Analysis]
63 Wu S, Mao P, Li R, Cai Z, Heidari AA, Xia J, Chen H, Mafarja M, Turabieh H, Chen X. Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis. Comput Biol Med 2021;135:104582. [PMID: 34214940 DOI: 10.1016/j.compbiomed.2021.104582] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 7.5] [Reference Citation Analysis]
64 Izci D. Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm. Transactions of the Institute of Measurement and Control 2021;43:3195-211. [DOI: 10.1177/01423312211019633] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 7.5] [Reference Citation Analysis]
65 Zhao S, Wang P, Heidari AA, Chen H, Turabieh H, Mafarja M, Li C. Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease. Comput Biol Med 2021;134:104427. [PMID: 34020128 DOI: 10.1016/j.compbiomed.2021.104427] [Cited by in Crossref: 37] [Cited by in F6Publishing: 42] [Article Influence: 18.5] [Reference Citation Analysis]
66 Said M, Shaheen AM, Ginidi AR, El-sehiemy RA, Mahmoud K, Lehtonen M, Darwish MMF. Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer. Processes 2021;9:627. [DOI: 10.3390/pr9040627] [Cited by in Crossref: 39] [Cited by in F6Publishing: 41] [Article Influence: 19.5] [Reference Citation Analysis]
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68 Song S, Wang P, Heidari AA, Wang M, Zhao X, Chen H, He W, Xu S. Dimension decided Harris hawks optimization with Gaussian mutation: Balance analysis and diversity patterns. Knowledge-Based Systems 2021;215:106425. [DOI: 10.1016/j.knosys.2020.106425] [Cited by in Crossref: 57] [Cited by in F6Publishing: 59] [Article Influence: 28.5] [Reference Citation Analysis]
69 Zhang Y, Liu R, Heidari AA, Wang X, Chen Y, Wang M, Chen H. Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing 2021;430:185-212. [DOI: 10.1016/j.neucom.2020.10.038] [Cited by in Crossref: 120] [Cited by in F6Publishing: 130] [Article Influence: 60.0] [Reference Citation Analysis]
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