Enhanced bonobo optimizer for optimizing dynamic photovoltaic models

• Autorzy: Eid Heba F. , Cuevas Erik

Identyfikatory

DOI

10.7494/csci.2024.25.3.5651

• Języki publikacji: EN

Abstrakty

EN

Bonobo optimizer (BO) is a novel metaheuristic algorithm motivated by the social behaviour of the bonobos. This paper presents a quantum behaved bonobo optimization algorithm (QBOA) employing an innovative metaheuristic based on the reproductive strategies and social behavior of bonobos. Whereby, the quantum mechanics are embedded into the bonobo optimizer to direct the search agents through the search space. Accordingly, under this quantum-behaved movement, the proposed QBOA’s exploitation capability is promoted. The performance of the proposed QBOA is exhibited on CEC2005 and CEC2019 benchmarks. Moreover, the QBOA algorithm was adapted to optimize the dynamic photovoltaic models parameters. QBOA exhibits the efficiency and adequacy to solve various optimization problems based on experimental and comparison findings, as well as its ability to implement competitive and promising results optimizing dynamic photovoltaic models.

Słowa kluczowe

EN

bonobo optimization algorithm; quantum behaved mechanism; photovoltaic model; dynamic photovoltaic model; parameters estimation

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2024
• Tom: T. 25 (3)
• Strony: 469--493
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Eid Heba F.

• Al-Azhar University, Faculty of Science, Cairo, Egypt

autor

Cuevas Erik

• University of Guadalajara, Department of Electronics, Mexico

Bibliografia

• [1] AbdelAty A.M., Radwan A.G., Elwakil A.S., Psychalinos C.: Transient and Steady-State Response of a Fractional-Order Dynamic PV Model Under Different Loads, Journal of Circuits, Systems and Computers, vol. 27(02), 1850023, 2018. doi: 10.1142/S0218126618500238.
• [2] Ahmadianfar I., Bozorg-Haddad O., Chu X.: Gradient-based optimizer: A new metaheuristic optimization algorithm, Information Sciences, vol. 540, pp. 131–159, 2020. doi: 10.1016/j.ins.2020.06.037.
• [3] Ali M.Z., Awad N.H., Suganthan P.N., Duwairi R.M., Reynolds R.G.: A novel hybrid Cultural Algorithms framework with trajectory-based search for global numerical optimization, Information Sciences, vol. 334–335, pp. 219–249, 2016. doi: 10.1016/j.ins.2015.11.032.
• [4] Ayang A., Wamkeue R., Ouhrouche M., Djongyang N., Salomé N.E., Pombe J.K., Ekemb G.: Maximum likelihood parameters estimation of single-diode model of photovoltaic generator, Renew Energy, vol. 130, pp. 111–121, 2019. doi: 10.1016/ j.renene.2018.06.039.
• [5] Chou J.S., Truong D.N.: A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean, Applied Mathematics and Computation, vol. 389, 125535, 2021. doi: 10.1016/j.amc.2020.125535.
• [6] Das A.K., Pratihar D.K.: A new Bonobo Optimizer (BO) for real-parameter optimization. In: 2019 IEEE Region 10 Symposium (TENSYMP), pp. 108–113, 2019. doi: 10.1109/tensymp46218.2019.8971108.
• [7] De Waal F.B.: Bonobo Sex and Society, Scientific American, vol. 272, pp. 82–88, 1995. doi: 10.1038/scientificamerican0395-82.
• [8] Di Piazza M.C., Luna M., Vitale G.: Dynamic PV Model Parameter Identification by Least-Squares Regression, IEEE Journal of Photovoltaics, vol. 3, pp. 799–806, 2013. doi: 10.1109/jphotov.2012.2236146.
• [9] Di Piazza M.C., Vitale G.: Photovoltaic field emulation including dynamic and partial shadow conditions, Applied Energy, vol. 87, pp. 814–823, 2010.
• [10] Eberhart R., Kennedy J.: A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43, Nagoya, Japan, 1995.
• [11] Eid H.F., Abraham A.: Solving unconstrained, constrained optimization and constrained engineering problems using reconfigured water cycle algorithm, Evolutionary Intelligence, vol. 16, pp. 633–649, 2023. doi: 10.1007/s12065-021-00688-6.
• [12] Eid H.F., Muda A.K.: Adjustive Reciprocal Whale Optimization Algorithm for Wrapper Attribute Selection and Classification, International Journal of Image, Graphics and Signal Processing, vol. 11, pp. 18–26, 2019. doi: 10.5815/ ijigsp.2019.03.03.
• [13] El-Fergany A.: Efficient tool to characterize photovoltaic generating systems using mine blast algorithm, Electric Power Components and Systems, vol. 43(8–10), pp. 890–901, 2015. doi: 10.1080/15325008.2015.1014579.
• [14] Et-torabi K., Nassar-eddine I., Obbadi A., Errami Y., Rmaily R., Sahnoun S., El fajri A., Agunaou M.: Parameters estimation of the single and double diode photovoltaic models using a Gauss–Seidel algorithm and analytical method: A comparative study, Energy Conversion and Management, vol. 148, pp. 1041–1054, 2017. doi: 10.1016/j.enconman.2017.06.064.
• [15] Gil-Arias O., Ortiz-Rivera E.I.: General purpose tool for simulating the behavior of PV solar cells modules and arrays. In: 2008 11th Workshop on Control and Modeling for Power Electronics, pp. 1–5, 2008. doi: 10.1109/ compel.2008.4634686.
• [16] Go S.I., Choi J.H.: Design and Dynamic Modelling of PV-Battery Hybrid Systems for Custom Electromagnetic Transient Simulation, Electronics, vol. 9, 1651, 2020. doi: 10.3390/electronics9101651.
• [17] Holland J.H.: Genetic algorithms, Scholarpedia, vol. 7(12), 1482, 2012. doi: 10.4249/scholarpedia.1482.
• [18] Levin F.S.: An introduction to quantum theory, Cambridge University Press, 2002.
• [19] Liang J.J., Suganthan P.N., Deb K.: Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 68–75, 2005. doi: 10.1109/SIS.2005.1501604.
• [20] Mirjalili S.: Moth-flame optimization algorithm: a novel natureinspired heuristic paradigm, Knowledge-Based Systems, vol. 89, pp. 228–249, 2015. doi: 10.1016/ j.knosys.2015.07.006.
• [21] Mirjalili S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete and multi-objective problems, Neural Computing and Applications, vol. 27, pp. 1053–1073, 2016. doi: 10.1007/s00521-015-1920-1.
• [22] Mirjalili S., Gandomi A.H., Mirjalili S.Z., Saremi S., Faris H., Mirjalili S.M.: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, vol. 114, pp. 163–191, 2017. doi: 10.1016/ j.advengsoft.2017.07.002.
• [23] Mirjalili S., Lewis A.: The whale optimization algorithm, Advances in Engineering Software, vol. 95, pp. 51–67, 2016. doi: 10.1016/j.advengsoft.2016.01.008.
• [24] Pierezan J., Dos Santos Coelho L.: Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 2633–2640, Brazil, Rio de Janeiro, 2018. doi: 10.1109/cec.2018.8477769.
• [25] Price K.V., Awad N.H., Ali M.Z., Suganthan P.N.: Problem Definitions and Evaluation Criteria for the 100-Digit Challenge Special Session and Competition on Single Objective Numerical Optimization, Technical report, Nanyang Technological University, Singapore, 2018.
• [26] Radwan A.G., Salama K.N.: Fractional-order, RC and and RL circuits, Circuits, Systems, and Signal Processing, vol. 31, pp. 1901–1915, 2012. doi: 10.1007/ s00034-012-9432-z.
• [27] Rashedi E., Nezamabadi-Pour H., Saryazdi S.: GSA: a gravitational search algorithm, Information Sciences, vol. 179(13), pp. 2232–2248, 2009. doi: 10.1016/ j.ins.2009.03.004.
• [28] Ridha H.M., Heidari A.A., Wang M., Chen H.: Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models, Energy Conversion and Management, vol. 209, 112660, 2020. doi: 10.1016/ j.enconman.2020.112660.
• [29] Tossa A.K., Soro Y.M., Azoumah Y., Yamegueu D.: A new approach to estimate the performance and energy productivity of photovoltaic modules in real operating conditions, Solar Energy, vol. 110, pp. 543–560, 2014. doi: 10.1016/ j.solener.2014.09.043.
• [30] Zhao W., Wang L., Zhang Z.: Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm, Neural Computing & Applications, vol. 32, pp. 9383–9425, 2020. doi: 10.1007/s00521-019-04452-x.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).