An evolutionary Nelder–Mead slime mould algorithm with random learning for efficient design of photovoltaic models

Weng, Xuemeng; Heidari, Ali Asghar; Liang, Guoxi; Chen, Huiling; Ma, Xiaotong

doi:10.1016/j.egyr.2021.11.019

Cited by 26 publications

(8 citation statements)

References 119 publications

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“…2 of 21 swarm [17] and evolution [18][19][20][21][22][23], usually require significant computational efforts to converge and depend strongly upon the proper tuning of control parameters, such as population size, the maximum number of function evaluations (MaxNFEs) or iterations, searching strategies and so on. Hybrid algorithms [24][25][26][27][28][29] combine the strengths of two or more different methods in an attempt to achieve better results. For example, the ABC-TRR [29] algorithm combined the global exploration capability of the artificial bee colony (ABC) algorithm and the local exploitation ability of trust-region reflective (TRR) search to extract the parameters of SDM and DDM.…”

Section: Introductionmentioning

confidence: 99%

Opposite Normalized Trust-Region Reflective (ONTRR): A New Algorithm for Parameter Extraction of Single, Double and Triple Diode Solar Cell Models

et al. 2023

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To accurately, efficiently and reliably extract the parameters of single, double and triple diode solar cell models, this paper proposes a randomly initialized opposite normalized trust-region reflective (ONTRR) algorithm. The novelty of ONTRR lies primarily in two amendments to the standard TRR search. (1) Random opposite initialization is added to decrease the initial point sensitivity of TRR and thus reduce the possibility of being trapped in local optima. (2) Min-max normalization is embedded to eliminate the negative effects arising from different magnitudes of model parameter values and thus drive the derivative-dependent TRR search in an efficient manner. The proposed ONTRR algorithm is evaluated and compared to other state-of-the-art algorithms using four benchmarked I–V datasets with two commonly used objective functions. To be objective and reproducible, the comparative experiments are carried out with default random seeds for 1000 independent runs instead of the 30, 50, or 100 runs used in most studies in the literature. The comparison results demonstrate that for all 12 of the test cases, the proposed ONTRR algorithm consistently achieves the highest accuracy with the least computational effort, and is often superior to the best-performing algorithms reported in the literature in terms of convergence speed, average accuracy and statistical stability.

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Section: Introductionmentioning

confidence: 99%

Opposite Normalized Trust-Region Reflective (ONTRR): A New Algorithm for Parameter Extraction of Single, Double and Triple Diode Solar Cell Models

et al. 2023

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“…ISMA is an upgraded version of the SMA that has been offered as a technique to precisely and effectively extract the unknown features of the solar cell [ 79 ]. ISMA was developed as a direct successor to the SMA.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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“…In the NGO algorithm, a random learning mechanism is introduced in the second step of goshawk position update. This mechanism involves selecting three different individuals randomly from the population to update the position of the whole population [26,27] . The expression is as follows:…”

Section: Random Learning Mechanismmentioning

confidence: 99%

Parameter identification of solar cell model based on improved northern goshawk optimization algorithm

Xiao,

Zhou,

et al. 2024

Preprint

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In this study, an improved northern goshawk optimization algorithm called SR-NGO is developed to solve the parameter identification problem of the Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM). Compared to the NGO algorithm, the SR-NGO algorithm reduces the minimum, maximum, mean, and standard deviation values of the root mean square error between experimental I-V data and the SDM(DDM, TDM) fitting data by 0.0004284% (0.2542%, 0.2034%), 1.1094% (3.7313%, 6.0740%), 0.05106% (0.6901%, 1.0699%), and 99.9999% (88.1540%, 94.0600%), respectively. Furthermore, the SR-NGO algorithm exhibits higher accuracy, faster convergence speed, and stronger stability compared to other existing algorithms. These results demonstrate that the SR-NGO algorithm, which combines the sine cosine guiding mechanism and random learning mechanism, enhances both local and global search capabilities, thereby improving the ability to escape local optima.

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An evolutionary Nelder–Mead slime mould algorithm with random learning for efficient design of photovoltaic models

Cited by 26 publications

References 119 publications

Opposite Normalized Trust-Region Reflective (ONTRR): A New Algorithm for Parameter Extraction of Single, Double and Triple Diode Solar Cell Models

Opposite Normalized Trust-Region Reflective (ONTRR): A New Algorithm for Parameter Extraction of Single, Double and Triple Diode Solar Cell Models

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Parameter identification of solar cell model based on improved northern goshawk optimization algorithm

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