Genetic Algorithm Feature Research Articles

Digital background calibration algorithm for pipelined ADC based on time-delay neural network with genetic algorithm feature selection

This paper presents a novel background calibration method for pipelined analog-to-digital converters (ADCs) using a time-delay neural network (TDNN), which is optimized through genetic algorithm (GA) techniques. The proposed technique leverages TDNN to create enhanced feature sets, significantly improving the calibration of nonlinear errors exhibiting memory effects. It harnesses the GA's global optimization capabilities for feature selection, effectively reducing the feature dimension and consequently alleviating the NN's computational burden. A parallel pipeline architecture is devised for the calibration circuit, with its implementation realized on FPGA to facilitate forward inference processing. The inference circuit is synthesized using TSMC's 90 nm CMOS process, achieving a power consumption of 40.11 mW and an area of 0.45 mm2. Simulations based on MATLAB for a 14-bit Pipelined ADC demonstrate that the proposed calibration method significantly improves the SFDR from 59.77 dB to 165.52 dB, and ENOB from 8.79 bits to 19.23 bits, surpassing the target ADC's specifications. Moreover, the dimensionality of features is effectively reduced by up to 34 % without compromising the calibration performance.

Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.

Prevention of credit card fraud transaction using GA feature selection for web-based application

Credit card fraud (CCF) is a regular event that generates financial losses. A considerable share of the significantly increased volume of internet transactions is made with credit cards. CCF detection programmes are consequently highly prioritised by banks and other financial organisations. These fraudulent transactions can come in a wide variety of formats and categories. To maintain data integrity, financial institutions support digital transactions. One of the most popular ways to pay the products and services can be done by both online and offline by using a credit card. Thus, there is a higher possibility of fraud during these financial transactions. This informs programmers to the requirement for a reliable technique for identifying successful fraud. Credit card users and businesses that accept credit cards have recently had to contend with the serious issue of CCF. Application-level frauds and transaction level frauds are the two categories into which CCF controlled frauds are divided. Therefore, utilizing genetic algorithm (GA) feature selection for web-based applications, it is advised to use this strategy as a method for the prevention of CCF transaction. This method's performance is evaluated based on a number of factors, including accuracy, recall, and specificity.

Genetic algorithm feature selection resilient to increasing amounts of data imputation

This paper investigates the robustness of a genetic algorithm (GA) in feature selection across a dataset with increasing imputed missing values. Feature selection can be beneficial in predictive modeling to reduce computational costs and potentially improve performance. Beyond these benefits, it also enables a clearer understanding of the algorithm's decision-making processes. In the context of real-world datasets that can contain missing values, feature selection becomes more challenging.A robust feature selection algorithm should be able to identify the key features despite missing data values. We investigate the effectiveness of this approach against two other feature selection algorithms on a dataset with increasingly imputed values to determine whether it can sustain good performance with only the selected features.Our results reveal that compared to the other two methods, the features selected by GA resulted in better classification performance across different imputation rates and methods.

Using Genetic Algorithm Feature Selection to Optimize XGBoost Performance in Australian Credit

To reduce credit risk in credit institutions, credit risk management practices need to be implemented so that lending institutions can survive in the long term. Data mining is one of the techniques used for credit risk management. Where data mining can find information patterns from big data using classification techniques with the resulting level of accuracy. This research aims to increase the accuracy of classification algorithms in predicting credit risk by applying genetic algorithms as the best feature selection method. Thus, the most important feature will be used to search for credit risk information. This research applies a classification method using the XGBoost classifier on the Australian credit dataset, then carries out an evaluation by measuring the level of accuracy and AUC. The results show an increase in accuracy of 2.24%, with an accuracy value of 89.93% after optimization using a genetic algorithm. So, through research on genetic algorithm feature selection, we can improve the accuracy performance of the XGBoost algorithm on the Australian credit dataset.

Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data

The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company, so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the WDC dataset with 30 features and 569 data points, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy and the use of weighted macros on precision, recall, and f1 score, using GA selection features has slightly higher results than RFE, so it is concluded that GA selection features are better at solving problems in high-dimensional data.

Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction

Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction

Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.

Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures

The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simulating the processes of ontophylogenetic synthesis of multi-agent neurocognitive architectures has been developed and experiments have been carried out to create phenotypes of intelligent agents based on them. A complex genome of an intelligent agent has been developed, the features of a multichromosome genetic algorithm for organizing calculations in the paradigm of multigenerational optimization of multiagent neurocognitive architectures have been established and substantiated. It is shown that multigenerational optimization of the multi-agent neurocognitive architecture of intelligent agents can contribute to the achievement of adaptive resistance to the operating conditions of a general artificial intelligence agent, provide the synthesis of its suboptimal structural and functional scheme, accelerate learning and algorithms for finding solutions to a universal range of problems solved by this agent in its ecological niche.

Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.

Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes. Recent advancements in computer vision, particularly the Swin transformer, have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). The Swin transformer adopts a hierarchical and efficient approach using shifted windows, enabling the capture of both local and global contextual information in images. In this paper, we propose a novel approach called Swin-GA-RF to enhance the classification performance of cervical cells in Pap smear images. Swin-GA-RF combines the strengths of the Swin transformer, genetic algorithm (GA) feature selection, and the replacement of the softmax layer with a random forest classifier. Our methodology involves extracting feature representations from the Swin transformer, utilizing GA to identify the optimal feature set, and employing random forest as the classification model. Additionally, data augmentation techniques are applied to augment the diversity and quantity of the SIPaKMeD1 cervical cancer image dataset. We compare the performance of the Swin-GA-RF Transformer with pre-trained CNN models using two classes and five classes of cervical cancer classification, employing both Adam and SGD optimizers. The experimental results demonstrate that Swin-GA-RF outperforms other Swin transformers and pre-trained CNN models. When utilizing the Adam optimizer, Swin-GA-RF achieves the highest performance in both binary and five-class classification tasks. Specifically, for binary classification, it achieves an accuracy, precision, recall, and F1-score of 99.012, 99.015, 99.012, and 99.011, respectively. In the five-class classification, it achieves an accuracy, precision, recall, and F1-score of 98.808, 98.812, 98.808, and 98.808, respectively. These results underscore the effectiveness of the Swin-GA-RF approach in cervical cancer classification, demonstrating its potential as a valuable tool for early diagnosis and screening programs.

Variable Selection for Recurrent Events Using Heuristic Approaches: Identifying Informative Variables for Rehospitalization in Schizophrenia Patients

Introduction: Recurrent event data, as a generalization of survival data, are frequently observed in various areas of medical research, including sequential hospitalizations in patients with schizophrenia. As experiencing multiple relapses during schizophrenia can have many implications, such as self-harm or harm to others, loss of education or employment, or other adverse outcomes, identifying and determining the most critical factors related to relapses in this disorder is essential. This study aimed to utilize heuristic approaches for selecting predictor variables in the field of recurrent events with an application to schizophrenia disorder
 Methods: A two-step algorithm was employed to apply a combination of two variable selection methods, recursive feature elimination (RFE) and genetic algorithm feature selection (GAFS), and four modeling techniques: Gradient boosting (GB), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) to simulated recurrent event datasets.
 Results: In most simulation scenarios, the results indicated that the combination of RFE and RF applied to the deviance residual (DR) outperforms the other methods. The RFE-RF-DR selected the following predictor variables: Number of children, age, marital status, and history of substance abuse.
 Conclusion: Our findings revealed that the proposed machine learning-based model is a promising technique for selecting predictor variables associated with a recurrent outcome when analyzing multivariate time-toevent data with recurrent events.

Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 resulted in the COVID-19 pandemic, necessitating rapid and accurate detection of pathogens through protein sequence data. This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. We utilized a dataset comprising 2000 protein sequences, including 1000 SARS-CoV-2 sequences and 1000 non-SARS-CoV-2 sequences. Feature extraction provided 27 essential features representing the primary structural data, achieved through the Discere package. To optimize performance, we employed machine learning classification algorithms such as K-nearest neighbor (KNN), XGBoost, and Naïve Bayes, along with feature selection techniques like genetic algorithm (GA), LASSO, and support vector machine recursive feature elimination (SVM-RFE). The SVM-RFE+KNN model exhibited exceptional performance, achieving a classification accuracy of 99.30%, specificity of 99.52%, and sensitivity of 99.55%. These results demonstrate the model’s efficacy in accurately classifying coronavirus protein sequences. Our research successfully developed a robust classification model capable of early detection and prediction of protein sequences in SARS-CoV-2 and other coronaviruses. This advancement holds great promise in facilitating the development of targeted treatments and preventive strategies for combating future viral outbreaks.

Machine Learning Model For Stunting Prediction

This study aims to find the best Supervised Machine Learning (SML) model for stunting prediction. This research was conducted using an experimental approach using 192 infant data with a composition of 183 normal infant data and 9 stunted infant data using a custom dataset. The conclusion obtained from this study can be concluded that the combination of the Random Forest classification algorithm with Support Vector Machine Weighting and the Genetic Algorithm Feature Selection has the best performance. The parameters with the best performance are: The training and testing data distribution is 90% of the training data and 10% of the testing data. The number of trees in the random forest algorithm is 100, and the Gain Ratio criterion and max_depth is 10. In the Genetic Algorithm, the best parameters are: The Roulette Wheel selection method, the population is 20, the mutation value is 0.03, and the crossover value is 0.9. The validation method uses k-fold cross validation with a value of k = 10. Another conclusion is that there are 44 supporting factors for stunting, which, if we take a ranking of 10 in order of magnitude from largest to smallest, the supporting factors for stunting are 1.Baby's weight at birth. 2.Baby’s Height at Birth. 3.Number of meal per day. 4.Breast Milk. 5.Diarrhe times per 3 month. 6.Child development examination during covid by Health Worker at home. 7.Mother's age at birth. 8.Mother height at birth. 9.Number of sibling. 10.Age when the first food was given. This research has the disadvantage of no test on other datasets. So researchers do not know the reliability of findings is on different datasets

Chirplet transform based time frequency analysis of speech signal for automated speech emotion recognition

Nowadays, the recognition of emotion using the speech signal has gained popularity because of its vast number of applications in different fields like medicine, online marketing, online search engines, the education system, criminal investigations, traffic collisions, etc. Many researchers have adopted different methodologies to improve emotion classification accuracy using speech signals. In our study, time–frequency (TF) analysis-based features were used to analyze the emotion classification performance. We used a novel TF analysis method called the chirplet transform (CT) to find the TF matrix of the speech signal. We then calculated the proposed TF-based permutation entropy (TFPE) feature using the TF matrix of the speech signal. To reduce the feature dimension and select the most informative emotional feature, we employed the genetic algorithm (GA) feature selection method. Then, the selected TFPE features are used as input to machine learning classifiers such as SVM, RF, DT, and KNN to classify the emotions in the speech signal. We obtained classification accuracy of 77.2%, 69.57%, 68.78%, 56.9%, and 99.1% for the EMO-DB, EMOVO, SAVEE, IEMOCAP, and TESS datasets without the GA feature selection method. The emotion classification accuracy increased to 85.6%, 78.33%, 77.76%, 63.15%, and 100% with the GA feature selection method. We compared our results with other methods and found that our method performed better in emotion classification than the state-of-the-art methods.

Classification of Electrocardiogram Signals using Deep Learning based on Genetic Algorithm Feature Extraction.

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias. We proposed a new ECG signal classification method to increase signal classification sensitivity. We smoothed the ECG signal with noise removal filters. A discrete wavelet transform based on an arrhythmic database was applied to extract ECG features. Feature vectors were obtained based on wavelet decomposition energy properties and calculated values of PQRS morphological features. We used the genetic algorithm to reduce the feature vector and determine the input layer weights of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Proposed methods for classifying ECG signals were in different classes of rhythm to diagnose heart rhythm diseases. Training data was with 80% of the data set and test data was with 20% for the whole data set. The learning accuracy for the results of training and test data in the ANN classifier was calculated as 99.9% and 88.92% and in ANFIS as 99.8% and 88.83% respectively. Based on these results, good accuracy was observed.

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment’s performance. This research’s aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R2 value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R2 values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R2 value among those that selected the fewest features.

Comparative study of feature selection methods for wind speed estimation at ungauged locations

Wind speed estimation at ungauged locations is one of the preliminary steps for wind resource assessment. With the availability of high-resolution Digital Elevation Models (DEM) and remote sensing data, the number of potential wind speed predictors has grown substantially. The adequate spatial scale of these predictors is unknown a priori, leading to the use of multiple spatial scales of predictors in wind speed estimation models. Implementing a feature selection method as a pre-processing step of the analysis is necessary to avoid overfitting and the resulting potential model underperformance. This paper evaluated six feature selection methods (forward stepwise regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, maximum relevance minimum redundancy (MRMR), Genetic algorithm, and recursive feature elimination using support vector regression) for the estimation of different wind speed quantiles across Canada. The selected features were used to fit a regression-kriging model, and the importance of the predictors was evaluated with their associated regression coefficients. The results of the study showed that LASSO and MRMR are the most efficient algorithms with the least number of parameters to tune and good generalization performance. The study found that some predictors were more important for specific exceedance probabilities. The most important predictors were the distance from the coast and surface roughness length, regardless of exceedance probability.

Recognition of Disease in Leaves Using Genetic Algorithm and Neural Network Based Feature Selection

Objectives : To suggest a suitable image recognition approach for the early recognition of leaf diseases using hybrid features with genetic algorithm and neural network feature selection technique to maximize the accuracy. Methods: Various image processing techniques are utilized to recognize disease in the leaf. In the pre-processing phase, CNN based de-noising is utilized to remove noise from the image. Next, disease part of the leaf is segmented by Pixel wise Classification approach with an optimization technique. Then, the colour and the segmented area of an image is used to remove out texture features. With GLCM and SIFT technique. Then the appropriate features are selected by Genetic Algorithm and Neural Network based feature selection. Extracted features are classified using an Ensemble classifier for recognizing disease in the leaf. For this experiment images are taken from the plant village data set. In this work, the disease caused by Alternaria solani and Pytopthara infestans pathogens is considered for recognizing the disease in the leaf. 1500 leaves are used. From each image, 1654 features are extracted. There is a 70% training and 30% test data split. Classification accuracy, precision and recall measures are considered for evaluating the proposed work’s performance. Findings: The proposed work gives 97.7% classification accuracy, 97.3% precision and 97.5% recall measures with various visual feature descriptors and GANN feature selection. Novelty: The in-depth investigation compares the proposed descriptor GANN_SVM and GANN ES detection and classification technique to local and global SVMs. The suggested descriptor outperforms current approaches for diagnosing Alternaria solani and Pytopthara infestans leaves and the method can be applied to all plants infected leaves. Keywords: Genetic Algorithm; Leaf disease detection; Image processing; Neural Network; ensemble classifier

Cross‐project defect prediction method based on genetic algorithm feature selection

AbstractWith the continuous development of Internet technology, the role of software in life is increasing, and software defect prediction (SDP) is a key means to ensure software reliability. SDP is to predict the modules that may have defects in advance based on the historical data of software projects, and its purpose is to maximize the use of testing resources. However, in the actual development process, the project that needs to be predicted is often a new project for which there is little or no historical data. Therefore, how to use the massive data of other related projects to build a cross‐project software defect prediction (CPDP) model has received extensive attention from scholars. However, due to the differences in data distribution and class imbalance between different projects, the performance of CPDP is greatly affected. Therefore, on the basis of CPDP, this article proposes a feature selection method based on genetic algorithm (genetic algorithm feature selection, GAFS). GAFS mainly includes two stages: feature selection and ensemble training. In the feature selection stage, this article proposes a global search adaptive feature selection method based on genetic algorithm, which uses the integrated training results of candidate feature subsets on target data to migrate the optimal feature subset. In the ensemble training phase, the EasyEnsemble method is used to alleviate the class imbalance problem, multiple naive Bayesian classifiers are constructed, and then the final model is constructed through ensemble learning. In this article, F1‐score and MCC are used as the test indicator, and comparative experiments are carried out on AEEEM and Promise. The results show that compared with the five comparison methods, GAFS can improve the average F1‐score and MCC much more. For example, GAFS can improve the average F1‐score value by 38.9%, 31.6%, 35.1%, 22.0%, and 31.6%, respectively. In most cases, it can effectively improve the performance of the model and achieve better prediction results.

Designing a Scenario-Based Fuzzy Model for Sustainable Closed-Loop Supply Chain Network considering Statistical Reliability: A New Hybrid Metaheuristic Algorithm

In this study, a new nonlinear mathematical programming model of mixed integer was presented to formulate the problem of designing a sustainable closed loop supply chain, in which the three aspects of sustainability, i.e., social effect such as job creation, customer satisfaction, and distributors, environmental effects such as reducing air pollution, and economic effects such as reducing supply chain costs, increasing supply chain reliability, quality of returned products by customers, and product routing were considered. In order to solve the proposed model, a new hybrid metaheuristic algorithm based on the distinctive features of gray wolf algorithm and genetic algorithm was proposed in addition to MOPSO and NSGA-II algorithms. After tuning their parameters by the Taguchi method, their performance in problems with different dimensions was tested and evaluated by MID, DM, and SM criteria. The results of statistical analysis of indices indicated that no significant difference between the performance of the three algorithms at 5% error level. In general, GW-NS, NSGA-II and MOPSO algorithms had better performance in terms of MID index, respectively. In addition, GW-NS, NSGA-II, and MOPSO algorithms performed better in terms of DM index. NSGA-II, MOPSO, and GW-NS algorithms performed better in terms of SM index, respectively. In addition, the variability of DM index in all three algorithms was almost the same, but in MID index, GW-NS algorithm, and in SM index, MOPSO algorithm had the highest change and less sustainability.