{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T12:30:44Z","timestamp":1769171444759,"version":"3.49.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2023,6,3]],"date-time":"2023-06-03T00:00:00Z","timestamp":1685750400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,3]],"date-time":"2023-06-03T00:00:00Z","timestamp":1685750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004750","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["201951096002"],"award-info":[{"award-number":["201951096002"]}],"id":[{"id":"10.13039\/501100004750","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["2020JQ-481"],"award-info":[{"award-number":["2020JQ-481"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00500-023-08575-1","type":"journal-article","created":{"date-parts":[[2023,6,3]],"date-time":"2023-06-03T03:29:13Z","timestamp":1685762953000},"page":"14835-14860","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel evolutionary algorithm inspired from triangle search and its applications on parameters identification of photovoltaic models"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2530-8365","authenticated-orcid":false,"given":"Zhenglei","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Cen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinzhi","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,3]]},"reference":[{"key":"8575_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2017.06.057","author":"A Abbassi","year":"2017","unstructured":"Abbassi A, Gammoudi R, Ali Dami M et al (2017) An improved single-diode model parameters extraction at different operating conditions with a view to modeling a photovoltaic generator: a comparative study. Sol Energy. https:\/\/doi.org\/10.1016\/j.solener.2017.06.057","journal-title":"Sol Energy"},{"key":"8575_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.10.069","author":"R Abbassi","year":"2019","unstructured":"Abbassi R, Abbassi A, Heidari AA, Mirjalili S (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag. https:\/\/doi.org\/10.1016\/j.enconman.2018.10.069","journal-title":"Energy Convers Manag"},{"key":"8575_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2015.05.074","author":"DF Alam","year":"2015","unstructured":"Alam DF, Yousri DA, Eteiba MB (2015) Flower pollination algorithm based solar PV parameter estimation. Energy Convers Manag. https:\/\/doi.org\/10.1016\/j.enconman.2015.05.074","journal-title":"Energy Convers Manag"},{"key":"8575_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2012.01.082","author":"MF AlHajri","year":"2012","unstructured":"AlHajri MF, El-Naggar KM, AlRashidi MR, Al-Othman AK (2012) Optimal extraction of solar cell parameters using pattern search. Renew Energy. https:\/\/doi.org\/10.1016\/j.renene.2012.01.082","journal-title":"Renew Energy"},{"key":"8575_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2016.06.052","author":"D Allam","year":"2016","unstructured":"Allam D, Yousri DA, Eteiba MB (2016) Parameters extraction of the three diode model for the multi-crystalline solar cell\/module using Moth-flame optimization algorithm. Energy Convers Manag. https:\/\/doi.org\/10.1016\/j.enconman.2016.06.052","journal-title":"Energy Convers Manag"},{"key":"8575_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2015.02.105","author":"F Almonacid","year":"2015","unstructured":"Almonacid F, Fern\u00e1ndez EF, Mallick TK, P\u00e9rez-Higueras PJ (2015) High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature. Energy. https:\/\/doi.org\/10.1016\/j.energy.2015.02.105","journal-title":"Energy"},{"key":"8575_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2011.04.013","author":"MR AlRashidi","year":"2011","unstructured":"AlRashidi MR, AlHajri MF, El-Naggar KM, Al-Othman AK (2011) A new estimation approach for determining the I-V characteristics of solar cells. Sol Energy. https:\/\/doi.org\/10.1016\/j.solener.2011.04.013","journal-title":"Sol Energy"},{"key":"8575_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.solmat.2013.11.011","author":"J Appelbaum","year":"2014","unstructured":"Appelbaum J, Peled A (2014) Parameters extraction of solar cells: a comparative examination of three methods. Sol Energy Mater Sol Cells. https:\/\/doi.org\/10.1016\/j.solmat.2013.11.011","journal-title":"Sol Energy Mater Sol Cells"},{"key":"8575_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2012.09.052","author":"A Askarzadeh","year":"2013","unstructured":"Askarzadeh A, Rezazadeh A (2013) Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2012.09.052","journal-title":"Appl Energy"},{"key":"8575_CR10","doi-asserted-by":"crossref","unstructured":"Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2016) An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: 2016 IEEE congress on evolutionary computation, CEC 2016. pp 2958\u20132965","DOI":"10.1109\/CEC.2016.7744163"},{"key":"8575_CR11","unstructured":"Awad NH, Ali MZ, Suganthan PN, Liang JJ and Qu BY (2017a) Problem definitions and evaluation criteria for the CEC 2017a special session and competition on single objective real-parameter numerical optimization. Nanyang Technol. University, Jordan University of Science and Technology, Zhengzhou University, Zhengzhou, China, Tech. Rep., 2016. [Online]"},{"key":"8575_CR12","doi-asserted-by":"crossref","unstructured":"Awad NH, Ali MZ, Suganthan PN (2017b) Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017b benchmark problems. In: 2017b IEEE congress on evolutionary computation, CEC 2017b \u2013 Proceedings","DOI":"10.1109\/CEC.2017.7969336"},{"key":"8575_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2011.12.085","author":"F Bonanno","year":"2012","unstructured":"Bonanno F, Capizzi G, Graditi G et al (2012) A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2011.12.085","journal-title":"Appl Energy"},{"key":"8575_CR14","doi-asserted-by":"crossref","unstructured":"Brest J, Mau\u010dec MS, Bo\u0161kovi\u0107 B (2016) IL-SHADE: improved L-SHADE algorithm for single objective real-parameter optimization. In: 2016 IEEE congress on evolutionary computation, CEC 2016","DOI":"10.1109\/CEC.2016.7743922"},{"key":"8575_CR15","doi-asserted-by":"crossref","unstructured":"Brest J, Mau\u010dec MS, Bo\u0161kovi\u0107 B (2017) Single objective real-parameter optimization: algorithm jSO. In: 2017 IEEE congress on evolutionary computation, CEC 2017 - proceedings","DOI":"10.1109\/CEC.2017.7969456"},{"key":"8575_CR16","doi-asserted-by":"crossref","unstructured":"Castillo O, Aguilar LT (2019) Genetic algorithms. In: Studies in fuzziness and soft computing","DOI":"10.1007\/978-3-030-03134-3_2"},{"key":"8575_CR17","doi-asserted-by":"publisher","first-page":"20083","DOI":"10.1007\/s00521-022-07558-x","volume":"34","author":"E \u00c7elik","year":"2022","unstructured":"\u00c7elik E, \u00d6zt\u00fcrk N, Houssein EH (2022) Influence of energy storage device on load frequency control of an interconnected dual-area thermal and solar photovoltaic power system. Neural Comput Appl 34:20083\u201320099. https:\/\/doi.org\/10.1007\/s00521-022-07558-x","journal-title":"Neural Comput Appl"},{"key":"8575_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.08.083","author":"Z Chen","year":"2016","unstructured":"Chen Z, Wu L, Lin P et al (2016) Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2016.08.083","journal-title":"Appl Energy"},{"key":"8575_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2019.02.032","author":"Z Chen","year":"2019","unstructured":"Chen Z, Chen Y, Wu L et al (2019) Accurate modeling of photovoltaic modules using a 1-D deep residual network based on I-V characteristics. Energy Convers Manag. https:\/\/doi.org\/10.1016\/j.enconman.2019.02.032","journal-title":"Energy Convers Manag"},{"key":"8575_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2013.02.017","author":"P Civicioglu","year":"2013","unstructured":"Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput. https:\/\/doi.org\/10.1016\/j.amc.2013.02.017","journal-title":"Appl Math Comput"},{"key":"8575_CR21","doi-asserted-by":"publisher","DOI":"10.1080\/01425918608909835","author":"T Easwarakhanthan","year":"1986","unstructured":"Easwarakhanthan T, Bottin J, Bouhouch I, Boutrit C (1986) Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int J Sol Energy. https:\/\/doi.org\/10.1080\/01425918608909835","journal-title":"Int J Sol Energy"},{"key":"8575_CR22","doi-asserted-by":"publisher","first-page":"17145","DOI":"10.1007\/s00521-022-07364-5","volume":"34","author":"A Eid","year":"2022","unstructured":"Eid A, Kamel S, Houssein EH (2022) An enhanced equilibrium optimizer for strategic planning of PV-BES units in radial distribution systems considering time-varying demand. Neural Comput Appl 34:17145\u201317173. https:\/\/doi.org\/10.1007\/s00521-022-07364-5","journal-title":"Neural Comput Appl"},{"key":"8575_CR23","doi-asserted-by":"crossref","unstructured":"Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl","DOI":"10.1007\/s00521-017-3272-5"},{"key":"8575_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2014.03.018","author":"AH Gandomi","year":"2014","unstructured":"Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans. https:\/\/doi.org\/10.1016\/j.isatra.2014.03.018","journal-title":"ISA Trans"},{"key":"8575_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-011-0241-y","author":"AH Gandomi","year":"2013","unstructured":"Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-011-0241-y","journal-title":"Eng Comput"},{"key":"8575_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2016.09.005","author":"X Gao","year":"2016","unstructured":"Gao X, Cui Y, Hu J et al (2016) Lambert W-function based exact representation for double diode model of solar cells: comparison on fitness and parameter extraction. Energy Convers Manag. https:\/\/doi.org\/10.1016\/j.enconman.2016.09.005","journal-title":"Energy Convers Manag"},{"key":"8575_CR27","doi-asserted-by":"publisher","unstructured":"Hadi AA, Wagdy A, and Jambi K (2018) Single-objective real parameter optimization: enhanced LSHADE-SPACMA algorithm. In: 2017 IEEE congress on evolutionary computation, CEC 2018 - Proceedings doi: https:\/\/doi.org\/10.13140\/RG.2.2.33283.20005","DOI":"10.13140\/RG.2.2.33283.20005"},{"key":"8575_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2975078","author":"Q Hao","year":"2020","unstructured":"Hao Q, Zhou Z, Wei Z, Chen G (2020) Parameters identification of photovoltaic models using a multi-strategy success-history-based adaptive differential evolution. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.2975078","journal-title":"IEEE Access"},{"key":"8575_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115253","author":"EH Houssein","year":"2021","unstructured":"Houssein EH, Mahdy MA, Fathy A, Rezk H (2021a) A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2021.115253","journal-title":"Expert Syst Appl"},{"key":"8575_CR30","doi-asserted-by":"publisher","first-page":"107304","DOI":"10.1016\/j.compeleceng.2021.107304","volume":"94","author":"EH Houssein","year":"2021","unstructured":"Houssein EH, Zaki GN, Diab AAZ, Younis EMG (2021b) An efficient Manta Ray Foraging Optimization algorithm for parameter extraction of three-diode photovoltaic model. Comput Electr Eng 94:107304. https:\/\/doi.org\/10.1016\/j.compeleceng.2021.107304","journal-title":"Comput Electr Eng"},{"key":"8575_CR31","doi-asserted-by":"publisher","first-page":"13961","DOI":"10.1002\/er.8114","volume":"46","author":"EH Houssein","year":"2022","unstructured":"Houssein EH, Nassef AM, Fathy A et al (2022) Modified search and rescue optimization algorithm for identifying the optimal parameters of high efficiency triple-junction solar cell\/module. Int J Energy Res 46:13961\u201313985. https:\/\/doi.org\/10.1002\/er.8114","journal-title":"Int J Energy Res"},{"key":"8575_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2012.05.017","author":"K Ishaque","year":"2012","unstructured":"Ishaque K, Salam Z, Mekhilef S, Shamsudin A (2012) Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2012.05.017","journal-title":"Appl Energy"},{"key":"8575_CR33","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/12\/11\/322","author":"JA Jervase","year":"2001","unstructured":"Jervase JA, Bourdoucen H, Al-Lawati A (2001) Solar cell parameter extraction using genetic algorithms. Meas Sci Technol. https:\/\/doi.org\/10.1088\/0957-0233\/12\/11\/322","journal-title":"Meas Sci Technol"},{"key":"8575_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2013.06.004","author":"LL Jiang","year":"2013","unstructured":"Jiang LL, Maskell DL, Patra JC (2013) Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2013.06.004","journal-title":"Appl Energy"},{"key":"8575_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-007-9149-x","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim. https:\/\/doi.org\/10.1007\/s10898-007-9149-x","journal-title":"J Glob Optim"},{"key":"8575_CR36","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.advengsoft.2017.03.014","volume":"110","author":"A Kaveh","year":"2017","unstructured":"Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69\u201384. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.03.014","journal-title":"Adv Eng Softw"},{"key":"8575_CR37","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.future.2017.10.052","volume":"81","author":"M Kumar","year":"2018","unstructured":"Kumar M, Kulkarni AJ, Satapathy SC (2018) Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Futur Gener Comput Syst 81:252\u2013272. https:\/\/doi.org\/10.1016\/j.future.2017.10.052","journal-title":"Futur Gener Comput Syst"},{"key":"8575_CR38","doi-asserted-by":"crossref","unstructured":"Kumar A, Misra RK, Singh D (2017) Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. In: 2017 IEEE congress on evolutionary computation, CEC 2017 - Proceedings","DOI":"10.1109\/CEC.2017.7969524"},{"key":"8575_CR39","unstructured":"Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Proceedings of MENDEL 2000, 6th international mendel conference on soft computing"},{"key":"8575_CR40","doi-asserted-by":"crossref","unstructured":"Lezama F, Soares J, Faia R, Vale Z (2019) Hybrid-adaptive differential evolution with decay function (Hyde-DF) applied to the 100-digit challenge competition on single objective numerical optimization. In: GECCO 2019 companion - proceedings of the 2019 genetic and evolutionary computation conference companion","DOI":"10.1145\/3319619.3326747"},{"key":"8575_CR41","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.asoc.2015.11.015","volume":"39","author":"M Li","year":"2016","unstructured":"Li M, Zhao H, Weng X, Han T (2016) Cognitive behavior optimization algorithm for solving optimization problems. Appl Soft Comput J 39:199\u2013222. https:\/\/doi.org\/10.1016\/j.asoc.2015.11.015","journal-title":"Appl Soft Comput J"},{"key":"8575_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105680","author":"Y Liang","year":"2019","unstructured":"Liang Y, Wang X, Zhao H et al (2019) A covariance matrix adaptation evolution strategy variant and its engineering application. Appl Soft Comput J. https:\/\/doi.org\/10.1016\/j.asoc.2019.105680","journal-title":"Appl Soft Comput J"},{"key":"8575_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04225-6","volume-title":"Innovations in swarm intelligence","author":"CP Lim","year":"2009","unstructured":"Lim CP, Jain LC, Dehuri S (2009) Innovations in swarm intelligence. Springer, Cham"},{"key":"8575_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2015.09.028","author":"J Meng","year":"2015","unstructured":"Meng J, Feng J, Sun Q et al (2015) Degradation model of the orbiting current for GaInP\/GaAs\/Ge triple-junction solar cells used on satellite. Sol Energy. https:\/\/doi.org\/10.1016\/j.solener.2015.09.028","journal-title":"Sol Energy"},{"key":"8575_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2018.10.006","author":"AW Mohamed","year":"2019","unstructured":"Mohamed AW, Hadi AA, Jambi KM (2019) Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Swarm Evol Comput. https:\/\/doi.org\/10.1016\/j.swevo.2018.10.006","journal-title":"Swarm Evol Comput"},{"key":"8575_CR46","doi-asserted-by":"crossref","unstructured":"Mohamed AW, Hadi AA, Fattouh AM, Jambi KM (2017) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation, CEC 2017 - Proceedings","DOI":"10.1109\/CEC.2017.7969307"},{"key":"8575_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.06.040","author":"F Mohammadi","year":"2018","unstructured":"Mohammadi F, Abdi H (2018) A modified crow search algorithm (MCSA) for solving economic load dispatch problem. Appl Soft Comput J. https:\/\/doi.org\/10.1016\/j.asoc.2018.06.040","journal-title":"Appl Soft Comput J"},{"key":"8575_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124363","author":"AM Nassef","year":"2022","unstructured":"Nassef AM, Houssein EH, din Helmy BE, Rezk H (2022a) Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization. Energy. https:\/\/doi.org\/10.1016\/j.energy.2022.124363","journal-title":"Energy"},{"key":"8575_CR49","doi-asserted-by":"publisher","first-page":"7242","DOI":"10.1016\/j.egyr.2022.05.231","volume":"8","author":"AM Nassef","year":"2022","unstructured":"Nassef AM, Houssein EH, Helmy BE et al (2022b) Optimal reconfiguration strategy based on modified Runge Kutta optimizer to mitigate partial shading condition in photovoltaic systems. Energy Rep 8:7242\u20137262. https:\/\/doi.org\/10.1016\/j.egyr.2022.05.231","journal-title":"Energy Rep"},{"key":"8575_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2014.06.026","author":"Q Niu","year":"2014","unstructured":"Niu Q, Zhang L, Li K (2014a) A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells. Energy Convers Manag. https:\/\/doi.org\/10.1016\/j.enconman.2014.06.026","journal-title":"Energy Convers Manag"},{"key":"8575_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhydene.2013.12.110","author":"Q Niu","year":"2014","unstructured":"Niu Q, Zhang H, Li K (2014b) An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models. Int J Hydrogen Energy. https:\/\/doi.org\/10.1016\/j.ijhydene.2013.12.110","journal-title":"Int J Hydrogen Energy"},{"key":"8575_CR52","doi-asserted-by":"crossref","unstructured":"Pandey HM (2016) Jaya a novel optimization algorithm: What, how and why? In: Proceedings of the 2016 6th international conference - cloud system and big data engineering, confluence 2016","DOI":"10.1109\/CONFLUENCE.2016.7508215"},{"key":"8575_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.01.027","author":"SJ Patel","year":"2014","unstructured":"Patel SJ, Panchal AK, Kheraj V (2014) Extraction of solar cell parameters from a single current-voltage characteristic using teaching learning based optimization algorithm. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2014.01.027","journal-title":"Appl Energy"},{"key":"8575_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2016.11.007","author":"S Pindado","year":"2017","unstructured":"Pindado S, Cubas J (2017) Simple mathematical approach to solar cell\/panel behavior based on datasheet information. Renew Energy. https:\/\/doi.org\/10.1016\/j.renene.2016.11.007","journal-title":"Renew Energy"},{"key":"8575_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2010.12.015","author":"RV Rao","year":"2011","unstructured":"Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. CAD Comput Aided Des. https:\/\/doi.org\/10.1016\/j.cad.2010.12.015","journal-title":"CAD Comput Aided Des"},{"key":"8575_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.03.004","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (NY). https:\/\/doi.org\/10.1016\/j.ins.2009.03.004","journal-title":"Inf Sci (NY)"},{"key":"8575_CR57","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Glob Optim"},{"key":"8575_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2017.01.046","author":"A Tabari","year":"2017","unstructured":"Tabari A, Ahmad A (2017) A new optimization method: electro-search algorithm. Comput Chem Eng. https:\/\/doi.org\/10.1016\/j.compchemeng.2017.01.046","journal-title":"Comput Chem Eng"},{"key":"8575_CR59","doi-asserted-by":"crossref","unstructured":"Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: Proceedings of the 2014 IEEE congress on evolutionary computation, CEC 2014. pp 1658\u20131665","DOI":"10.1109\/CEC.2014.6900380"},{"key":"8575_CR60","doi-asserted-by":"crossref","unstructured":"Wang GG, Deb S, Coelho LDS (2016) Elephant herding optimization. In: Proceedings - 2015 3rd international symposium on computational and business intelligence, ISCBI 2015","DOI":"10.1109\/ISCBI.2015.8"},{"key":"8575_CR61","doi-asserted-by":"publisher","DOI":"10.1080\/09540090210144948","author":"B Webb","year":"2002","unstructured":"Webb B (2002) Swarm intelligence: from natural to artificial systems. Conn Sci. https:\/\/doi.org\/10.1080\/09540090210144948","journal-title":"Conn Sci"},{"key":"8575_CR62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2918406","author":"Z Wei","year":"2019","unstructured":"Wei Z, Huang C, Wang X et al (2019b) Nuclear reaction optimization: a novel and powerful physics-based algorithm for global optimization. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2019.2918406","journal-title":"IEEE Access"},{"key":"8575_CR63","doi-asserted-by":"crossref","unstructured":"Wei Z, Huang C, Wang X, Zhang H (2019a) Parameters identification of photovoltaic models using a novel algorithm inspired from nuclear reaction. In: 2019a IEEE congress on evolutionary computation, CEC 2019a - Proceedings","DOI":"10.1109\/CEC.2019.8790223"},{"key":"8575_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.09.161","author":"L Wu","year":"2018","unstructured":"Wu L, Chen Z, Long C et al (2018) Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2018.09.161","journal-title":"Appl Energy"},{"key":"8575_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2017.08.063","author":"K Yu","year":"2017","unstructured":"Yu K, Liang JJ, Qu BY et al (2017) Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Convers Manag. https:\/\/doi.org\/10.1016\/j.enconman.2017.08.063","journal-title":"Energy Convers Manag"},{"key":"8575_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.06.010","author":"K Yu","year":"2018","unstructured":"Yu K, Liang JJ, Qu BY et al (2018) Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2018.06.010","journal-title":"Appl Energy"},{"key":"8575_CR67","doi-asserted-by":"publisher","DOI":"10.1007\/s12293-019-00286-1","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Huang C, Dong K, Huang H (2019) Birds foraging search: a novel population-based algorithm for global optimization. Memet Comput. https:\/\/doi.org\/10.1007\/s12293-019-00286-1","journal-title":"Memet Comput"},{"key":"8575_CR68","doi-asserted-by":"crossref","unstructured":"Zolghadr-Asli B, Bozorg-Haddad O, Chu X (2018) Crow search algorithm (CSA). In: Studies in computational intelligence","DOI":"10.1007\/978-981-10-5221-7_14"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08575-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-08575-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08575-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T12:07:48Z","timestamp":1693570068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-08575-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,3]]},"references-count":68,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8575"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-08575-1","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,3]]},"assertion":[{"value":"17 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}