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Hybrid Coyote Optimization with Differential Evolution and its Application to the Estimation of Solar Photovoltaic Cell Parameters

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Jul 11, 2025

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G. Xu, Q. Cui, X. Shi, H. Ge, Z. Zhan, H. Lee, Y. Liang, R. Tai, C. Wu, Particle swarm optimization based on dimensional learning strategy, Swarm Evol. Comput. 45 (2019) 33–51. Search in Google Scholar

R. Storn, K. Price, Differential evolution – a simple and efcient heuristic for global optimization over continuous spaces, J. Global Optim. 11 (4) (1997) 341–359. https://doi.org/http://dx.doi.org/10.1023/A:1008202821328 doi: http://dx.doi.org/10.1023/A:1008202821328. Search in Google Scholar

S. Mirjalili, A. Mirgalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Softw. 69 (3) (2014) 46–61. https://doi.org/http://dx.doi.org/10.1016/j.advengsoft.2013.12.007 doi: http://dx.doi.org/10.1016/j.advengsoft.2013.12.007. Search in Google Scholar

Y. Zheng, M. Zhang, B. Zhang, Biogeographic harmony search for emergencyair transportation, Soft Comput. 20 (3) (2016) 967–977. https://doi.org/http://dx.doi.org/101007/s00500-014-1556-6 doi: http://dx.doi.org/101007/s00500-014-1556-6. Search in Google Scholar

X. Zhang, Q. Lin, Three-learning strategy particle swarm algorithm for global optimization problems, Information Sciences 593 (2022) 289–313. Search in Google Scholar

X. Xia, Y. Xing, B. Wei, Y. Zhang, X. Li, X. Deng, L. Gui, A fitness-based multi-role particle swarm optimization, Swarm Evol. Comput. (2019) 349–364. Search in Google Scholar

Z.-G. Liu, X.-H. Ji, Y. Yang, H.-T. Cheng, Multi-technique diversity-based particle-swarm optimization, Inf. Sci. 577 (4) (2021) 298–323. Search in Google Scholar

H.-Q. Xu, S. Gu, Y.-C. Fan, X.-S. Li, Y.-F. Zhao, J. Zhao, J.-J. Wang, A strategy learning framework for particle swarm optimization algorithm, Information Sciences 619 (2023) 126–152. Search in Google Scholar

M. Wang, Y. Ma, A differential evolution algorithm based on accompanying population and piecewise evolution strategy, Appl. Soft Comput. 143 (2023) 110390. Search in Google Scholar

H. Li, H. Kang, Y. Pang, G. Sun, S. Liang, Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning, Applied Soft Computing 165 (2024) 112121. Search in Google Scholar

Y. Zhang, X. Gu, A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time, Expert Systems with Applications 231 (2023) 120732. Search in Google Scholar

X. Xia, L. Tong, Y. Zhang, X. Xu, H. Yang, L. Gui, Y. Li, K. Li, Nfdde: A novelty-hybrid-fitness driving differential evolution algorithm, Inf. Sci. (N. Y.) 579 (2021) 33–54. https://doi.org/https://doi.org/10.1016/j.ins.2021.07.082 doi: https://doi.org/10.1016/j.ins.2021.07.082. Search in Google Scholar

H. Song, J. Bei, H. Zhang, J. Wang, P. Zhang, Hybrid algorithm of differential evolution and flower pollination for global optimization problems, Expert Systems with Applications 237, Part A (2024) 121402. https://doi.org/https://doi.org/10.1016/j.eswa.2023. 121402 doi: https://doi.org/10.1016/j.eswa.2023.121402. Search in Google Scholar

H. Abdel-Nabi, M. Z. Ali, A. Awajan, R. Alazrai, M. I. Daoud, P. N. Suganthan, An iterative cyclic tri-strategy hybrid stochastic fractal with adaptive differential algorithm for global numerical optimization, Information Sciences 628 (2023) 92–133. https://doi.org/https://doi.org/10.1016/j.ins.2023.01.065 doi: https://doi.org/10.1016/j.ins.2023.01.065. Search in Google Scholar

X. Zhang, Q. Kang, J. Cheng, X. Wang, A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer, Appl. Soft Comput. 67 (2018) 197–214. https://doi.org/https://doi.org/10.1016/j.asoc.2018.02.049 doi: https://doi.org/10.1016/j.asoc.2018.02.049. Search in Google Scholar

A. Abdelshafy, H. Hassan, J. Jurasz, Optimal design of a grid-connected desalination plant powered by renewable energy resources using a hybrid pso-gwo approach, Energy Convers. Manage. 173 (2018) 331–347. https://doi.org/http://dx.doi.org/10.1016/j.enconman.2018.07.083 doi: http://dx.doi.org/10.1016/j.enconman.2018.07.083. Search in Google Scholar

W. Gong, Z. Cai, C. Ling, De/bbo: A hybrid differential evolution with biogeography-based optimization for global numerical optimization, Soft Comput 15 (4) (2011) 645–665. https://doi.org/https://doi.org/10.1007/s00500-010-0591-1 doi: https://doi.org/10.1007/s00500-010-0591-1. Search in Google Scholar

W. Deng, R. Chen, J. Gao, Y. Song, J. Xu, A novel parallel hybrid intelligence optimization algorithm for a function approximation problem, Comput. Math. Appl. 63 (1) (2012) 325–336. https://doi.org/http://dx.doi.org/10.1016/j.camwa.2011.11.028 doi: http://dx.doi.org/10.1016/j.camwa.2011.11.028. Search in Google Scholar

X. Chen, H. Tianfield, C. Mei, W. Du, G. Liu, Biogeography-based learning particle swarm optimization for continuous optimization problems, Soft Comput. 21, 24 (2016) 7519–7541. https://doi.org/https://doi.org/10.1007/s00500-016-2307-7 doi: https://doi.org/10.1007/s00500-016-2307-7. Search in Google Scholar

Z. Teng, J. Lv, L. Guo, An improved hybrid grey wolf optimization algorithm, Soft Comput. 23, 15 (2019) 6617–6631. https://doi.org/http://dx.doi.org/10.1007/s00500-018-3310-y doi: http://dx.doi.org/10.1007/s00500-018-3310-y. Search in Google Scholar

D. Wolpert, W. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput. 1 (1) (1997) 67–82. Search in Google Scholar

J. Pierezan, L. Coelho, Coyote optimization algorithm: a new metaheuristic for global optimization problems, in: IEEE Congress Evol. Comput., Brazil, 2018, pp. 1–8. Search in Google Scholar

H. Alghamdi, Optimum placement of distribution generation units in power system with fault current limiters using improved coyote optimization algorithm, Entropy 23 (6) (2021) 655. https://doi.org/https://doi.org/10.3390/e23060655 doi: https://doi.org/10.3390/e23060655. Search in Google Scholar

H. Rezk, A. Fathy, M. Aly, A robust photovoltaic array reconfiguration strategy based on coyote optimization algorithm for enhancing the extracted power under partial shadow condition, Energy Rep. 7 (2021) 109–124. Search in Google Scholar

M. H. Q. et al, Coyote optimization algorithm for parameters extraction of three-diode photo-voltaic models of photovoltaic modules, Energy 187 (2019). Search in Google Scholar

A. Draa, S. Bouzoubia, I. Boukhalfa, A sinusoidal differential evolution algorithm for numerical optimization, Appl. Soft Comput. 27 (2014) 99–126. https://doi.org/http://dx.doi.org/10.1016/j.asoc.2014.11.003 doi: http://dx.doi.org/10.1016/j.asoc.2014.11.003. Search in Google Scholar

N. Awad, M. Ali, P. Suganthan, J. Liang, B. Qu, Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization, Tech. rep., Nanyang Technological University, Singapore and Jordan University of Science and Technology, Jordan and Zhengzhou University, Zhengzhou China (2017). Search in Google Scholar

J. Liang, B. Qu, P. Suganthan, A. Hernandez-Diaz, Problem definitions and evaluation criteria for the cec-2013 special session on real-parameter optimization, Tech. rep., Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore (2013). Search in Google Scholar

Z. Q. C. J, Differential mutation and novel social learning particle swarm optimization algorithm, Information Sciences: An International Journal 480 (2019). Search in Google Scholar

I. Aydilek, A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems, Appl. Soft Comput. 66 (2018) 232–249. https://doi.org/http://dx.doi.org/10.1016/j.asoc.2018.02.025 doi: http://dx.doi.org/10.1016/j.asoc.2018.02.025. Search in Google Scholar

J. Gou, Y. Lei, W. Guo, C. Wang, Y. Cai, W. Luo, A novel improved particle swarm optimization algorithm based on individual difference evolution, Appl. Soft Comput. 57 (2017) 468–481. Search in Google Scholar

X. Chen, H. Tianfield, C. Mei, W. Du, G. Liu, Biogeography-based learning particle swarm optimization, Soft Comput. 21, 24 (2017) 7519–7541. https://doi.org/10.1007/s00500-016-2307-7 doi:10.1007/s00500-016-2307-7. Search in Google Scholar

J. Li, J. Zhang, C. Jiang, M. Zhou, Composite particle swarm optimizer with historical memory for function optimization, IEEE Trans. Cybernetics 45 (10) (2015) 2350–2363. https://doi.org/10.1109/TCYB.2015.2424836 doi:10.1109/TCYB.2015.2424836. Search in Google Scholar

X. Zhang, S. Wen, Heap-based optimizer based on three new updating strategies, Expert Systems with Applications 209 (2022) 118222. Search in Google Scholar

A. M. Shaheen, A. M. Elsayed, A. R. Ginidi, R. A. El-Sehiemy, E. Elattar, A heap-based algorithm with deeper exploitative feature for optimal allocations of distributed generations with feeder reconfiguration in power distribution networks, Knowledge-Based Systems 241 (2022) 108269. https://doi.org/10.1016/j.knosys.2022.108269 doi:10.1016/j.knosys.2022.108269. Search in Google Scholar

X. M. Zhang, X. Wang, H. Y. Chen, D. D. Wang, Z. H. Fu, Improved gwo for large-scale function optimization and mlp optimization in cancer identification, Neural Computing and Applications 32 (2020) 1305–1325. https://doi.org/10.100s00521-019-04483-4 doi:10.100s00521-019-04483-4. Search in Google Scholar

Q. Tu, X. C. Chen, X. C. Liu, Multi-strategy ensemble grey wolf optimizer and its application to feature selection, Appl. Soft Comput. 76 (2019) 16–30. https://doi.org/10.1016/j.asoc.2018.11.047 doi:10.1016/j.asoc.2018.11.047. Search in Google Scholar

I. B. Aydilek, A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems, Appl. Soft Comput. 66 (2018) 232–249. https://doi.org/10.1016/j.asoc.2018.02.025 doi:10.1016/j.asoc.2018.02.025. Search in Google Scholar

Q. Zhang, J. Gao, H. Dong, Y. Mao, Wpd and de/bbo-rbfnn for solution of rolling bearing fault diagnosis, Neurocomputing 312 (2018) 27–33. Search in Google Scholar

X. Chen, K. J. Yu, Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters, Sol. Energy 180 (2019) 192–206. Search in Google Scholar

X. M. Zhang, Q. Kang, J. F. Cheng, X. Wang, A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer, Appl. Soft Comput. 67 (2018) 197–214. Search in Google Scholar

L. Z. Cui, K. Zhang, G. H. Li, X. H. Fu, Z. K. Wen, N. Lu, J. Lu, Modified gbest-guided artificial bee colony algorithm with new probability model, Soft Comput. 22 (7) (2018) 2217–2243. https://doi.org/10.1007/s00500-017-2485-y doi:10.1007/s00500-017-2485-y. Search in Google Scholar

S. Talatahari, M. Azizi, Chaos game optimization: a novel metaheuristic algorithm, Artificial Intelligence Review 54 (2021) 917–1004. Search in Google Scholar

P. Korosec, J. Silc, The continuous differential ant-stigmergy algorithm applied on real-parameter single objective optimization problems, in: 2013 IEEE Congress on Evolutionary Computation, 2013, p. 7. Search in Google Scholar

S. Dhabal, P. Venkateswaran, An efficient gbest-guided cuckoo search algorithm for higher order two channel filter bank design, Swarm Evol. Comput. 33 (2017) 68–84. https://doi.org/10.1016/j.swevo.2016.10.003 doi:10.1016/j.swevo.2016.10.003. Search in Google Scholar

J. Derrac, S. García, D. Molina, F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol. Comput. 1 (1) (2011) 3–18. Search in Google Scholar

J. C. H. Phang, D. S. H. Chan, J. R. Phillips, Accurate analytical method for the extraction of solar cell model parameters, Electron Lett. 20 (10) (1984) 406. https://doi.org/10.1049/el:1984028110 doi:10.1049/el:1984028110. Search in Google Scholar

V. J. Chin, Z. Salam, K. Ishaque, An accurate and fast computational algorithm for the two-diode model of pv module based on a hybrid method, IEEE Trans. Ind Electron. 64 (8) (2017) 6212–6222. https://doi.org/10.1109/TIE.2017.2682023 doi:10.1109/TIE.2017.2682023. Search in Google Scholar

P. Changmai, S. K. Nayak, S. K. Metya, Estimation of pv module parameters from the manufacturer’s datasheet for mpp estimation, IET Renew Power Gener. 14 (11) (2020) 1988–1996. https://doi.org/10.1049/iet-rpg.2019.1377 doi:10.1049/iet-rpg.2019.1377. Search in Google Scholar

X. Yang, W. Gong, Opposition-based jaya with population reduction for parameter estimation of photovoltaic solar cells and modules, Appl Soft Comput. 104 (2021) 107218. https://doi.org/10.1016/j.asoc.2021.107218 doi:10.1016/j.asoc.2021.107218. Search in Google Scholar

L. Chaib, M. Tadj, A. Choucha, F. Z. Khemili, A. EL-Fergany, mproved crayfish optimization algorithm for parameters estimation of photovoltaic models, Energy Conversion and Management 313 (2024) 118627. Search in Google Scholar

K. M. Hosny, A. A. A. ElMageed, A. A. Abohany, R. M. Hussein, M. Gaffar, Precise estimation of solar photovoltaic parameters via brown bear optimization and differential evolution, Alexandria Engineering Journal 127 (2025) 164–199. Search in Google Scholar

M. Premkumar, S. Ravichandran, T. J. T. Hashim, T. C. S. Hussein, R. Abbassi, Fitness-guided particle swarm optimization with adaptive newton-raphson for photovoltaic model parameter estimation, Applied Soft Computing 167 (2024) 112295. Search in Google Scholar

C. Kumar, T. D. Raj, M. Premkumar, T. D. Raj, A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters, Optik 223 (2020) 165277. Search in Google Scholar

G. Xiong, J. Zhang, D. Shi, L. Zhu, X. Yuan, Z. Tan, Winner leading competitive swarm optimizer with dynamic gaussian mutation for parameter extraction of solar photovoltaic models, Energy Convers Manage. 206 (2020) 112450. https://doi.org/10.1016/j.enconman.2019.112450 doi:10.1016/j.enconman.2019.112450. Search in Google Scholar

G. Xiong, J. Zhang, D. Shi, Y. He, Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm, Energy Convers Manage. 174 (2018) 388–405. https://doi.org/10.1016/j.enconman.2018.08.053 doi:10.1016/j.enconman.2018.08.053. Search in Google Scholar

K. Yu, J. J. Liang, B. Y. Qu, X. Chen, H. Wang, Parameters identification of photovoltaic models using an improved jaya optimization algorithm, Energy Convers Manage. 150 (2017) 742–753. https://doi.org/10.1016/j.enconman.2017.08.063 doi:10.1016/j.enconman.2017.08.063. Search in Google Scholar

M. Abdel-Basset, D. El-Shahat, R. K. Chakrabortty, M. Ryan, Parameter estimation of photovoltaic models using an improved marine predators algorithm, Energy Convers Manage. 227 (2021) 113491. https://doi.org/10.1016/j.enconman.2020.113491 doi:10.1016/j.enconman.2020.113491. Search in Google Scholar

M. Naeijian, A. Rahimnejad, S. M. Ebrahimi, N. Pourmousa, S. A. Gadsden, Parameter estimation of pv solar cells and modules using whippy harris hawks optimization algorithm, Energy Rep. 7 (2021) 4047–4063. https://doi.org/10.1016/j.egyr.2021.06.085 doi:10.1016/j.egyr.2021.06.085. Search in Google Scholar

Z. W, W. P, H. AA, et al., Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules, Energy Rep 7 (2021) 5175–5202. https://doi.org/10.1016/j.egyr.2021.07.041 doi:10.1016/j.egyr.2021.07.041. Search in Google Scholar

Y. W, L. P, H. C, Simplified swarm optimization for the solar cell models parameter estimation problem, IET Renew Power Gener 11 (8) (2017) 1166–1173. https://doi.org/10.1049/iet-rpg.2016.0473 doi:10.1049/iet-rpg.2016.0473. Search in Google Scholar

C. X, Y. K, D. W, Z. W, L. G, Parameters identification of solar cell models using generalized oppositional teaching learning based optimization, Energy 99 (2016) 170–180. https://doi.org/10.1016/j.energy.2016.01.052 doi: 10.1016/j.energy.2016.01.052. Search in Google Scholar

X. G, Z. J, S. D, Z. L, Y. X, Parameter extraction of solar photovoltaic models with an either-or teaching learning based algorithm, Energy Convers Manage 224 (2020). https://doi.org/10.1016/j.enconman.2020.113395 doi:10.1016/j.enconman.2020.113395. Search in Google Scholar

L. JJ, Q. AK, S. PN, B. S, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans Evol Comput 10 (3) (2006) 281–295. https://doi.org/10.1109/TEVC.2005.857610 doi: 10.1109/TEVC.2005.857610. Search in Google Scholar

Language:
English
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Journal Subjects:
Computer Sciences, Databases and Data Mining, Artificial Intelligence