Average Fitness Research Articles

An Improved Light Spectrum Optimizer for Parameter Identification of Triple-Diode PV Model

Over the last few decades, researchers have paid attention to finding an effective and efficient metaheuristic algorithm that can determine the ideal parameters for PV models. In this study, to determine the TDM’s nine unknown parameters, we will examine the efficacy of a recently proposed metaheuristic algorithm called light spectrum optimizer (LSO). To further enhance the effectiveness of LSO in estimating those unknown parameters, a new improved variant called ILSO is developed. This variant employs LSO in conjunction with two newly developed update systems to improve its exploration and exploitation operators. We compare the best fitness value, worst fitness value, average fitness, standard deviation, and p-value returned by the Wilcoxon rank-sum test obtained by LSO and ILSO to those of three recently published competitors when estimating the nine unknown parameters for the Photowatt-PWP201 module and the RTC France solar cell. The experimental findings show that ILSO is the most efficient.

A study on the preparation conditions of lidocaine microemulsion based on multi-objective genetic algorithm.

Background: Topical lidocaine microemulsion preparations with low toxicity, low irritation, strong transdermal capability and convenient administration are urgently needed. Methods: Box-Behnken design was performed for three preparation conditions of 5% lidocaine microemulsions: mass ratio of the mass ratio of surfactant/(oil phase + surfactant) (X1), the mass ratio of olive oil/(α-linolenic acid + linoleic acid) (X2) and the water content W% (X3). Then, five multi-objective genetic algorithms were used to optimize the three evaluation indices to optimize the effects of lidocaine microemulsion preparations. Finally, the ideal optimization scheme was experimentally verified. Results: Non-dominated Sorting Genetic Algorithm-II was used for 30 random searches. Among these, Scheme 2: X1 = 0.75, X2 = 0.35, X3 = 75%, which resulted in Y1 = 0.17μg/(cm2·s) and Y2 = 0.74mg/cm2; and the Scheme 19: X1 = 0.68, X2 = 1.42, X3 = 75% which resulted in Y1 = 0.14μg/(cm2·s) and Y2 = 0.80mg/cm2, provided the best matches for the objective function requirements. The maximum and average fitness of the method have reached stability after 3 generations of evolution. Experimental verification of the above two schemes showed that there were no statistically significant differences between the measured values of Y1 and Y2 and the predicted values obtained by optimization (p > 0.05) and are close to the target value. Conclusion: Two lidocaine microemulsion preparation protocols were proposed in this study. These preparations resulted in good transdermal performance or long anesthesia duration, respectively.

An improved binary grey wolf optimizer for constrained engineering design problems

AbstractAn Improved binary Non‐Linear Convergent Bi‐phase Mutated Grey Wolf Optimizer (IbGWO) is proposed for solving feature selection problems with two main goals reducing irrelevant features and maximizing accuracy. We used stratified ‐fold cross‐validation that performs stratified sampling on the data to avoid overfitting problems. The fitness function used in the proposed algorithm allows choosing the solution with the minimum number of features if more than one feature has the same highest accuracy. When stratified cross‐validation is performed, the split datasets contain the same share of the feature of interest as the actual dataset. During stratified sampling, the cross‐validation result minimizes the generalization error to a considerable extent, with a smaller variance. Feature selection could be seen as an optimization problem that efficiently removes irrelevant data from high‐dimensional data to reduce computation time and improve learning accuracy. This paper proposes an improved Non‐Linear Convergent Bi‐Phase Mutated Binary Grey Wolf Optimizer (IbGWO) algorithm for feature selection. The bi‐phase mutation enhances the rate of exploitation of GWO, where the first mutation phase minimizes the number of features and the second phase adds more informative features for accurate feature selection. A non‐linear tangent trigonometric function is used for convergence to generalize better while handling heterogeneous data. To accelerate the global convergence speed, an inertia weight is added to control the position updating of the grey wolves. Feature‐weighted K‐Nearest Neighbor is used to enhance classification accuracy, where only relevant features are used for feature selection. Experimental results confirm that IbGWO outperforms other algorithms in terms of average accuracy of 0.8716, average number of chosen features of 6.13, average fitness of 0.1717, and average standard deviation of 0.0072 tested on different datasets and in terms of statistical analysis. IbGWO is also benchmarked using unimodal, multimodal, and IEEE CEC 2019 functions, where it outperforms other algorithms in most cases. Three classical engineering design problems are also solved using IbGWO, which significantly outperforms other algorithms. Moreover, the overtaking percentage of the proposed algorithm is .

RETRACTED ARTICLE: The Application of Artificial Immune Network in E-Commerce Credit Risk Assessment

In order to improve the accuracy of e-commerce credit risk assessment, this paper suggests utilizing an artificial immune network to upgrade the text mining algorithm. Through this process, a new e-commerce risk assessment model reliant on the improved algorithm can be constructed with the intention of decreasing the likelihood of risk in digital transactions. The results show that the accuracy and loss rate of the improved clustering algorithm are 97.3% and 4.3%, respectively, both of which are better than the comparison algorithm. Then, the empirical analysis of the e-commerce credit risk assessment model proposed in the study shows that the average fitness and accuracy of the model after stability are 0.0022 and 95.63%, respectively, demonstrating superior performance compared to the comparison model. The above results show that the improved algorithm and the risk assessment model have good performance. Therefore, using this model to evaluate the credit risk of e-commerce can not only improve the accuracy of credit evaluation and promote the sustainable development of e-commerce. Furthermore, it can catalyze the adoption of innovative credit evaluation methods and promote the application of artificial intelligence technology in e-commerce.

Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation

Archimedes' optimization algorithm (AOA) is a recently proposed physics-based optimization algorithm inspired by Archimedes' Principle. However, for optimization of complex engineering problems, it shows slow convergence and can easily fall into local minima. Chaotic maps have non-repeatability properties they can effectively scan the search space with fast convergence in optimization algorithms. In this study, knacks of physics-based optimization along with chaotic maps are exploited for the parameter estimation of autoregressive exogenous (ARX), nonlinear input ARX (NI-ARX), LD-Didactic temperature process plant models and electrically stimulated muscle model. Ten chaotic maps have been applied to AOA. The fitness function of both models is constructed based on the square error between true and estimated response. The chaotic variants indicate that AOA with a chaotic Circle map (CAOA2) gives the best performance in terms of accuracy and convergence against reptile search heuristics, sine cosine optimization approach, harris hawks optimizer (HHO) and whale optimization algorithm (WOA) for different variations of generation and noise levels. Statistics based on mean square error, convergence plots, statistical plots, Taguchi test, average fitness, t-test, and Friedman test for repeated measures approve the reliability of CAOA2 for parameter estimation of ARX, NI-ARX, LD-Didactic temperature process plant models and electrically stimulated muscle model.

RETRACTED ARTICLE: Research on Financial Risk Evaluation and Control of Tourism Enterprises Based on Improved GA Algorithm

With the change and complexity of the tourism market environment, the financial risks faced by tourism enterprises are increasingly diversified. Effectively evaluating and controlling these financial risks has become the key to the development of tourism enterprises. Therefore, this study builds an accurate and real-time enterprise financial risk assessment and control model with the help of genetic algorithm. The results show that compared with other models, the maximum error value of the research model is only 0.12, and the maximum mean square error is only 0.09. The high reliability of the model is verified by simulating the data of selected tourism enterprises. After increasing the number of samples, the accuracy of the model continues to improve, and the predicted financial indicators are more in line with the actual situation. The model achieves the best results in average fitness, and the required error value is reached within 10 iterations. In the goodness of fit comparison, the goodness of fit of the training set, the test set and the verification set of the model are all over 0.7. In the empirical analysis, the ACC of the research model reached 97.4%, the accuracy rate reached 97.1%, the F1 index of the research reached 98.6%, and the other three research models were all lower than 98%. The above shows that the research model has significant advantages and can effectively evaluate and control the financial risk of tourism enterprises.

Patterns of bacterial generalists and specialists in lakes and reservoirs along a latitudinal gradient

AbstractAimThe geographical distribution of bacteria is an important, but poorly understood, topic in microbial ecology. A major question is how broadly distributed generalist taxa, and limitedly distributed specialist taxa, vary across a latitudinal gradient in freshwater ecosystems. We predict that: (a) generalists and specialists exhibit latitudinal diversity gradient with different patterns; (b) their community assemblies are mainly driven by stochastic processes; and (c) generalists coexist through niche differences, while specialists coexist through fitness differences.LocationSixty lakes and reservoirs in China, ranging from 18° N to 50° N.Time PeriodJuly–August in 2012 and 2018.Taxa StudiedBacterioplankton.MethodsWe clustered zero‐radius operational taxonomic unit (zOTU) (subspecies‐like level) into operational taxonomic unit (OTU) (species‐like level) and calculated the intra‐specific variation and spatial asynchrony of OTU. We examined the relative effects of environment versus space (latitude) on bacterial community and quantified the community assembly processes of generalists and specialists, respectively. The binary‐state speciation and extinction (BiSSE) model has been used to estimate the evolutionary characteristics. The abundances of the top zOTUs within each OTU were identified to reveal their niche preferences (distribution patterns at different sites).ResultsWe found that bacterial generalists had a higher intra‐specific variation, greater asynchrony and wider niche width than the specialists. With increasing latitude, bacterial diversity decreased with abundance variations in generalists and occurrence variations in specialists, respectively. Diversification in stochastic processes was the main driving factor in both groups, though deterministic processes showed greater effects on specialists than generalists in local communities. Bacterial generalists coexisted through niche differences, whereas specialists coexisted through average fitness differences.Main ConclusionsWe demonstrated distinct latitudinal distribution patterns of bacterial generalists and specialists. These results highlight differences in biogeography of microbial generalist and specialist taxa mediated by intra‐specific variation and emphasize their distinctly vital roles mediated by differing mechanisms in freshwater ecosystems.

Self-loops in evolutionary graph theory: Friends or foes?

Evolutionary dynamics in spatially structured populations has been studied for a long time. More recently, the focus has been to construct structures that amplify selection by fixing beneficial mutations with higher probability than the well-mixed population and lower probability of fixation for deleterious mutations. It has been shown that for a structure to substantially amplify selection, self-loops are necessary when mutants appear predominately in nodes that change often. As a result, for low mutation rates, self-looped amplifiers attain higher steady-state average fitness in the mutation-selection balance than well-mixed populations. But what happens when the mutation rate increases such that fixation probabilities alone no longer describe the dynamics? We show that self-loops effects are detrimental outside the low mutation rate regime. In the intermediate and high mutation rate regime, amplifiers of selection attain lower steady-state average fitness than the complete graph and suppressors of selection. We also provide an estimate of the mutation rate beyond which the mutation-selection dynamics on a graph deviates from the weak mutation rate approximation. It involves computing average fixation time scaling with respect to the population sizes for several graphs.

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. The complexities of GS are especially noticeable in the context of microarray expression data analysis, owing to the inherent data imbalance. The main goal of this study is to offer a simplified and computationally effective approach to dealing with the conundrum of attribute selection in microarray gene expression data. We use the Black Widow Optimization algorithm (BWO) in the context of GS to achieve this, using two unique methodologies: the unaltered BWO variation and the hybridized BWO variant combined with the Iterated Greedy algorithm (BWO-IG). By improving the local search capabilities of BWO, this hybridization attempts to promote more efficient gene selection. A series of tests was carried out using nine benchmark datasets that were obtained from the gene expression data repository in the pursuit of empirical validation. The results of these tests conclusively show that the BWO-IG technique performs better than the traditional BWO algorithm. Notably, the hybridized BWO-IG technique excels in the efficiency of local searches, making it easier to identify relevant genes and producing findings with higher levels of reliability in terms of accuracy and the degree of gene pruning. Additionally, a comparison analysis is done against five modern wrapper Feature Selection (FS) methodologies, namely BIMFOHHO, BMFO, BHHO, BCS, and BBA, in order to put the suggested BWO-IG method's effectiveness into context. The comparison that follows highlights BWO-IG's obvious superiority in reducing the number of selected genes while also obtaining remarkably high classification accuracy. The key findings were an average classification accuracy of 94.426, average fitness values of 0.061, and an average number of selected genes of 2933.767.

Route Optimization of Electric Vehicles Based on Reinsertion Genetic Algorithm

The "mileage anxiety" of EVs has led to do the study on the electric vehicles routing problem (EVRP). In this paper, the remove-reinsert genetic algorithm (RI-GA) is applied to the EVRP with a time window (EVRPTW) for saving energy and reducing transportation costs. The initialization and the variation of the genetic algorithm are improved to obtain a routing schedule with less energy consumption. The neighborhood search method improves the population initialization to obtain high-quality initialized populations. Removal and reinsertion methods are proposed to destruct and repair the routing. During the removal process of RI-GA, the relevance removal strategy is designed. By comparing the correlation between nodes, the nodes with high correlation are removed simultaneously, providing directionality for the removal of the customer stops. The relationship between individual fitness and population average fitness is compared and the number of nodes removed is automatically adjusted. The minimum energy consumption incremental reinsertion method is employed in the reinsertion process of RI-GA to obtain the routing schedule with less energy consumption. Compared to the least-cost convergence of the original GA and RI-GA, the convergence speed of RI-GA is faster by 5 times, and the routing cost is reduced by 6%. The comparison with Solomons data demonstrates the advantages of the RI-GA, with a reduction of about 10% in route distance. The superiority of this improved algorithm becomes more apparent as the number of customer stops increases and the transportation routes become more complex, and the reduction in energy consumption increases from 7.95% to 12.33%. Finally, the performance of the RI-GA is verified in the apple transportation problem in Hudson Valley, New York, which proves the practicality of this algorithm.

Advantages of model averaging of species sensitivity distributions used for regulating produced water discharges.

Produced water (PW) generated by Australian offshore oil and gas activities is typically discharged to the ocean after treatment. These complex mixtures of organic and inorganic compounds can pose significant environmental risk to receiving waters, if not managed appropriately. Oil and gas operators in Australia are required to demonstrate that environmental impacts of their activity are managed to levels that are as low as reasonably practicable, for example, through risk assessments comparing predicted no-effect concentrations (PNECs) with predicted environmental concentrations of PW. Probabilistic species sensitivity distribution (SSD) approaches are increasingly being used to derive PW PNECs and subsequently calculating dilutions of PW (termed "safe" dilutions) required to protect a nominated percentage of species in the receiving environment (e.g., 95% and 99% or PC95 and PC99, respectively). Limitations associated with SSDs include fitting a single model to small (six to eight species) data sets, resulting in large uncertainty (very wide 95% confidence limits) in the region associated with PC99 and PC95 results. Recent advances in SSD methodology, in the form of model averaging, claim to overcome some of these limitations by applying the average model fit of multiple models to a data set. We assessed the advantages and limitations of four different SSD software packages for determining PNECs for five PWs from a gas and condensate platform off the North West Shelf of Australia. Model averaging reduced occurrences of extreme uncertainty around PC95 and PC99 values compared with single model fitting and was less prone to the derivation of overly conservative PC99 and PC95 values that resulted from lack of fit to single models. Our results support the use of model averaging for improved robustness in derived PNEC and subsequent "safe" dilution values for PW discharge management and risk assessment. In addition, we present and discuss the toxicity of PW considering the paucity of such information in peer-reviewed literature. Integr Environ Assess Manag 2024;20:498-517. © 2023 Commonwealth Scientific and Industrial Research Organisation. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Automatic classification method of power user’s requirements text based on parallel naive Bayesian algorithm

In order to ensure the efficiency of power user’s requirements processing, an automatic classification method for demand test of power users based on parallel naive Bayesian algorithm is proposed. Polynomial naive Bayes is selected to build Hadoop cluster, and the feature words of power user’s requirements are selected through chi square test. The weight of each feature item is calculated by word frequency-inverse text frequency index method, and the weight sum of each category is calculated. The weight sum is input into naive Bayes algorithm to output the text classification results of power user’s requirements. At the same time, The naive Bayes classification algorithm is parallelized and encapsulated to reduce the cost of data movement and exchange in the classification process, and improve the operation efficiency of demand text classification of power user. The experimental results show that this method can accurately extract the feature words of power user’s requirements, effectively realize the automatic classification of power user’s requirements text, and have a more accurate classification effect. The average fitness value of the proposed method tends to be stable after more than 20 training times, and the number of network convergence steps is 7. When the ratio of energy function is about 0.4 and 0.6, the average IU value is the highest. When the required number of texts ranges from 500 to 1500, the delay time of text classification is 0.02 s, and the peak signal-to-noise ratio is more than 33, among which the highest peak signal-to-noise ratio is 42.52, and the normalization coefficient is 1.

Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.

Navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network

Aiming at the shortcomings of the existing navigation line extraction algorithm for corn spraying robot in complex farmland environment, such as poor extraction effect and poor adaptability, this paper proposed a navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network. This algorithm proposed to use corn plant core as the identification target to locate corn plants. Firstly, a new spatial pyramid structure - atrous spatial pyramid pooling faster (ASPPF) was proposed in this paper, and an improved YOLOv8s model ASPPF-YOLOv8s was proposed for more accurate detection of corn plant cores. Secondly, the center coordinates of the network detection box were used to locate the corn crop row feature points. Finally, the least squares method was used to fit the corn crop row lines. Experimental results showed that ASPPF-YOLOv8s network had a good effect on corn plant cores extraction under different growth periods and environmental pressures. Mean average precision (MAP) and F1 of ASPPF-YOLOv8s network increased from 86.4% and 86% of YOLOv7 network, 88.8% and 87% of YOLOv8s network, 88.6% and 89% of ASPP-YOLOv8s network to 90.2% and 91%. The average fitting time and average angle error of the centerline of crop row were reduced from 82.6 ms and 0.97° for SUSAN corner detection method combined with least square method, 74.8 ms and 0.75° for means method combined with least square method, 67 ms and 2.03° for FAST corner detection method combined with least square method to 45 ms and 0.63° by using the center coordinates of corn plant core detection box combined with least square method. The accuracy rate increased from 91.47% for SUSAN corner detection method combined with least square method, 93.6% for means method combined with least square method and 87.35% for FAST corner detection method combined with least square method to 94.35%. It shows that the navigation line extraction algorithm proposed in this paper can meet the requirements of real-time and accuracy of visual navigation of corn spraying robot, and can be used to extract navigation line of corn spraying robot in complex farmland environment.

Validation of machine learning ridge regression models using Monte Carlo, bootstrap, and variations in cross-validation

Abstract In recent years, there have been several calls by practitioners of machine learning to provide more guidelines on how to use its methods and techniques. For example, the current literature on resampling methods is confusing and sometimes contradictory; worse, there are sometimes no practical guidelines offered at all. To address this shortcoming, a simulation study was conducted that evaluated ridge regression models fitted on five real-world datasets. The study compared the performance of four resampling methods, namely, Monte Carlo resampling, bootstrap, k-fold cross-validation, and repeated k-fold cross-validation. The goal was to find the best-fitting λ (regularization) parameter that would minimize mean squared error, by using nine variations of these resampling methods. For each of the nine resampling variations, 1,000 runs were performed to see how often a good fit, average fit, and poor fit λ value would be chosen. The resampling method that chose good fit values the greatest number of times was deemed the best method. Based on the results of the investigation, three general recommendations are made: (1) repeated k-fold cross-validation is the best method to select as a general-purpose resampling method; (2) k = 10 folds is a good choice in k-fold cross-validation; (3) Monte Carlo and bootstrap are underperformers, so they are not recommended as general-purpose resampling methods. At the same time, no resampling method was found to be uniformly better than the others.

A Gas Prominence Prediction Model Based on Entropy-Weighted Gray Correlation and MCMC-ISSA-SVM

To improve the accuracy of coal and gas prominence prediction, an improved sparrow search algorithm (ISSA) and an optimized support vector machine (SVM) based on the Markov chain Monte Carlo (MCMC) filling algorithm prediction model were proposed. The mean value of the data after filling in the missing values in the coal and gas prominence data using the MCMC filling algorithm was 2.282, with a standard deviation of 0.193. Compared with the mean fill method (Mean), random forest filling method (random forest, RF), and K-nearest neighbor filling method (K-nearest neighbor, KNN), the MCMC filling algorithm showed the best results. The parameter indicators of the salient data were ranked by entropy-weighted gray correlation analysis, and the salient prediction experiments were divided into four groups with different numbers of parameter indicators according to the entropy-weighted gray correlation. The best results were obtained in the fourth group, with a maximum relative error (maximum relative error, REmax) of 0.500, an average relative error (average relative error, MRE) of 0.042, a root mean square error (root mean square error, RMSE) of 0.144, and a coefficient of determination (coefficient of determination, R2) of 0.993. The best predicted parameters were the initial velocity of gas dispersion (X2), gas content (X4), K1 gas desorption (X5), and drill chip volume (X6). To improve the sparrow search algorithm (sparrow search algorithm, SSA), the adaptive t-distribution variation operator was introduced to obtain ISSA, and the prediction models of improved sparrow search algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC-ISSA-SVM), sparrow search algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC-SSA-SVM), genetic algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC-GA-SVM) and particle swarm optimization algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC- PSO -SVM) were established for coal and gas prominence prediction using the ISSA, SSA, genetic algorithm (genetic algorithm, GA) and particle swarm optimization algorithm (particle swarm optimization, PSO) respectively. Comparing the prediction experimental results of each model, the prediction accuracy of MCMC-ISSA-SVM is 98.25%, the error is 0.018, the convergence speed is the fastest, the number of iterations is the least, and the best fitness and the average fitness are the highest among the four models. All the prediction results of MCMC-ISSA-SVM are significantly better than the other three models, which indicates that the algorithm improvement is effective. ISSA outperformed SSA, PSO, and GA, and the MCMC-ISSA-SVM model was able to significantly improve the prediction accuracy and effectively enhance the generalization ability.

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate.

An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation.

Evaluation of the deviation zone based on discrete measured points is crucial for quality control in manufacturing and metrology. However, deviation-zone evaluation is a highly nonlinear problem that is difficult to solve using traditional numerical optimization methods. Swarm intelligence has many advantages in solving this problem: it produces gradient-free, high-quality solutions and is characterized by its ease of implementation. Therefore, this study applies an improved Harris hawks algorithm (HHO) to tackle the problem. The average fitness is applied to replace the random operator in the exploration phase to solve the problem of conflicting exploration strategies due to randomness. In addition, the salp swarm algorithm (SSA) with a nonlinear inertia weight is embedded into the HHO, such that the superior explorative ability of SSA can fill the gap in the exploration of HHO. Finally, the optimal solution is greedily selected between SSA-based individuals and HHO-based individuals. The effectiveness of the proposed improved HHO optimizer is checked through a comparison with other swarm intelligence methods in typical benchmark problems. Moreover, the experimental results of form deviation-zone evaluation on primitive geometries show that the improved method can accurately solve various form deviations, providing an effective general solution for primitive geometries in the manufacturing and metrology fields.

Analysis of differences in physical fitness levels of extracurricular futsal students: Survey studies on urban and rural environments

Background and Study Aim. Maintaining physical fitness is essential to perform daily tasks with the required level of efficiency. By being physically fit to design an ideal training plan. This study aims to determine the profile and differences in the level of physical fitness in futsal extracurricular students in urban and rural environments. Materials and Methods. The method in this study used a quantitative approach through surveys with researchers providing a physical test and measurement (TKJI) for 13-15 year old to futsal extracurricular students. This research was conducted at SMP Negeri 11 Pontianak City and SMP Negeri 3 Sukadana, Kayong Utara Regency. Purposive sampling technique in determining the sample so that 40 students were sampled. To see the difference through the normality prerequisite test stage, homogeneity test and t test. Data analysis was assisted by using the SPSS Version 26 application. Results. According to the study findings, the average physical fitness score of futsal extracurriculars in urban areas was 12.50, while the average in rural environments was 15.15. Furthermore, a significant difference between urban and rural extracurricular futsal contexts is shown by a significance value of 0.000 < 0.05. Conclusions. This finding supports the hypothesis that the level of physical fitness necessary for extracurricular futsal varies significantly between urban and rural areas. This study provides evidence that futsal extracurricular students in rural areas have better physical fitness than students in urban areas. These findings can be a reference for sports practitioners to be able to develop exercise programs to improve the physical fitness of children aged 13-15 years, especially for playing futsal.

A new two-axis solar tracker based on the online optimization method: Experimental investigation and neural network modeling

This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model. In this research, four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles, which determine the position of the sun. The magnitude of the sunray is considered as the cost function of all algorithms. Then, several experiments are carried out to find the best optimization algorithm with optimal population size, number of iterations, and also the best initialization method. Uniform initialization leads to faster convergence compared to random initialization. The results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms, with a convergence time of less than 40 s. The average fitness value or voltage received by the tracker is 2.4 Volts in this method, which is higher than other methods. TLBO also performs well with a population size of 15 and 7 iterations. Afterward, the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from PSO. Number of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network modeling. The performance of the proposed ANN model is evaluated using regression plots, demonstrating a strong correlation between predicted and target outputs. Finally, the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.