Electrical parameter computation of various photovoltaic models using an enhanced jumping spider optimization with chaotic drifts

Lakshmanan, M.; Kumar, C.; Joseph, X. Felix

doi:10.1007/s10825-022-01891-z

Cited by 10 publications

(5 citation statements)

References 109 publications

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“…The IWSO outperforms all current state-of-the-art algorithms. The Chaos-Based GBO (CGBO), 82 Adaptive MPA (AMPA), 112 Chaotic JAYA (CJAYA), 66 Success-History-Based Parameter Adaptation for Differential Evolution (SHADE) algorithm, 20 Time-Varying Adaptive PSO (TVAPSO), 113 Chaotic JSO (CJSO), 88 and WSO 105 4.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The IWSO outperforms all current state‐of‐the‐art algorithms. The Chaos‐Based GBO (CGBO), 82 Adaptive MPA (AMPA), 112 Chaotic JAYA (CJAYA), 66 Success‐History‐Based Parameter Adaptation for Differential Evolution (SHADE) algorithm, 20 Time‐Varying Adaptive PSO (TVAPSO), 113 Chaotic JSO (CJSO), 88 and WSO 105 are considered for the performance analysis. The simulations are run with MATLAB, loaded on a laptop with an i5 CPU running at 4.44 GHz and 8 GB of RAM.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

Lakshmanan¹,

Kumar²,

Jasper

2023

Optim Control Appl Methods

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In the modeling and designing of PhotoVoltaic (PV) systems, parameter characterization in PV cell/module models remains a crucial field of research. Diode‐based models, such as single‐diode model (SDM), double‐diode model (DDM), and the three‐diode model, are frequently employed, and SDM and DDM are the most significant models. As a result, the difference between the estimated and experimental current can be minimized by using an objective function to solve the parameter characterization of such models. Metaheuristic optimization algorithms have recently been employed to get around the difficulty of finding accurate and highly reliable outcomes quickly. As a result, this research modifies the fundamental SDM and DDM and considers an objective function based on the modified models. Additionally, an improved version of a novel metaheuristic algorithm called White Shark Optimizer (WSO) is proposed by modifying the force control parameters of the WSO, and a chaotic generator is infused to improve the exploitation ability of WSO. The modified algorithm is named IWSO and applied to extract the PV parameters. This paper uses the new objective function to compare the conventional and the modified PV models. The outcomes of the experiment demonstrated IWSO's dominance over competing algorithms. With an average Freidman's ranking test value of 1.171, the proposed IWSO is superior to all selected algorithms. The average accuracy of modified SDM and DDM is 12% better than the traditional PV models. According to the findings, IWSO's estimated parameter values are the best, with the smallest difference between estimated and experimental current.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

Lakshmanan¹,

Kumar²,

Jasper

2023

Optim Control Appl Methods

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show abstract

“…HHJSOA was compared with JSOA, jumping spider optimization with chaotic drifts (CJSOA) [22], Nonlinear-based chaotic harris hawks optimization (NCHHO) [23], An improved grey wolf optimizer (IGWO) [24], JSOA, and Tunicate Swarm Algorithm (TSA) [25]. Set the number of populations n = 30 and the dimension of the function D = 30 or D = 2.…”

Section: Hhjsoa Performance Analysismentioning

confidence: 99%

An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

Xu,

Hu,

Cao

et al. 2023

Computers, Materials &Amp; Continua

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The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today's complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimizationseeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the harris hawk optimization (HHO) and small hole imaging (HHJSOA). We use it in network traffic identification feature selection. First, the method incorporates the HHO escape energy factor and the hard siege strategy to form a new search strategy for HHJSOA. This location update strategy enhances the search range of the optimal solution of HHJSOA. We use small hole imaging to update the inferior individual. Next, the feature selection problem is coded to propose a jumping spiders individual coding scheme. Multiple iterations of the HHJSOA algorithm find the optimal individual used as the selected feature for KNN classification. Finally, we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 dataset. Experimental results show that compared with other algorithms for the UNSW-NB15 dataset, the improvement is at least 0.0705, 0.00147, and 1 on the accuracy, fitness value, and the number of features. In addition, compared with other feature selection methods for the same datasets, the proposed algorithm has faster convergence, better merit-seeking, and robustness. Therefore, HHJSOA can improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local optimum due to high-dimensional features.

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“…In recent years, the Whale Algorithm [26], Grey Wolf Optimization [27], Sparrow Search Algorithm (SSA) [28], and Jumping Spider Optimization Algorithm [29] were heuristic algorithms proposed based on the above ideas, which were efficient search capabilities. They have been applied in many fields [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Speaker Recognition Based on Dung Beetle Optimized CNN

Guo,

Qin,

Zhang

et al. 2023

Applied Sciences

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Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has many hyperparameters that are difficult to determine, making the model easily fall into local optimum or even fail to converge during the training process. Intelligent algorithms such as particle swarm optimization and genetic algorithms are used to solve the above problems. However, these algorithms perform poorly compared to the current emerging meta-heuristic algorithms. In this study, the dung beetle optimized convolution neural network (DBO-CNN) is proposed to identify the speakers for the first time, which is helpful in finding suitable hyperparameters for training. By testing the dataset of 50 people, it was demonstrated that the accuracy of the model was significantly improved by using this approach. Compared with the traditional CNN and CNN optimized by other intelligent algorithms, the average accuracy of DBO-CNN has increased by 1.22~4.39% and reached 97.93%.

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Electrical parameter computation of various photovoltaic models using an enhanced jumping spider optimization with chaotic drifts

Cited by 10 publications

References 109 publications

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

Speaker Recognition Based on Dung Beetle Optimized CNN

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