Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.

Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard approaches.Consequently,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been proposed.When using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to instability.Because of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance vs.eleven other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and bGAmethods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

The Internet of Things(IoT)is considered the next-gen connection network and is ubiquitous since it is based on the Internet.Intrusion Detection System(IDS)determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour.In this back-ground,the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification(EMML-CADC)model in an IoT environment.The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency.To attain this,the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform format.In addition,Enhanced Cat Swarm Optimization based Feature Selection(ECSO-FS)approach is followed to choose the optimal feature subsets.Besides,Mayfly Optimization(MFO)with Twin Support Vector Machine(TSVM),called the MFO-TSVM model,is utilized for the detection and classification of cyberattacks.Here,the MFO model has been exploited to fine-tune the TSVM variables for enhanced results.The performance of the proposed EMML-CADC model was validated using a benchmark dataset,and the results were inspected under several measures.The comparative study concluded that the EMML-CADC model is superior to other models under different measures.

Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data traffic.The system named intrusion detection systems(IDS)is broadly utilized for the enhancement of security posture in an IT infrastructure.An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers.Nowadays,machine learning(ML)-related techniques were used for detecting intrusion in IoTs IDSs.But,the IoT IDS mechanism faces significant challenges because of physical and functional diversity.Such IoT features use every attribute and feature for IDS self-protection unrealistic and difficult.This study develops a Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for IDS.The proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT environment.To attain this,the presented MM-WMVEDL technique implements min-max normalization to scale the input dataset.For feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)technique.In the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLSTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDS-2017 dataset to exhibit the promising performance of the MM-WMVEDL technique.The comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image sensors as an automa-tion tool for the IIoT is increasingly becoming more common.Due to the fact that this organisation transfers an enormous number of photographs at any one time,one of the most significant issues that it has is reducing the total quantity of data that is sent and,as a result,the available bandwidth,without compromising the image quality.Image compression in the sensor,on the other hand,expedites the transfer of data while simultaneously reducing bandwidth use.The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties.The image encryp-tion model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system.This helps to ensure that image datasets are kept safe.The Linde–Buzo–Gray(LBG)methodology is an example of a vector quantization algorithm that is extensively used and a rela-tively new form of picture reduction known as vector quantization(VQ).As a result,the purpose of this research is to create an artificial humming bird optimi-zation approach that combines LBG-enabled codebook creation and encryption(AHBO-LBGCCE)for use in an IIoT setting.In the beginning,the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ.The Burrows-Wheeler Transform(BWT)model is used in order to accomplish codebook compression.In addition,the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained.A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms.The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained environment.WSN offers independent access to the target region and performs data collection in an effective manner.But energy constraints remain a challenging issue in WSN since it operates on in-built battery.The studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of WSN.Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms.Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN.In this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for WSN.Primarily,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of clusters.Besides,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the destination.Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters.The proposed IDTOMHR model was experimentally validated for its performance under different aspects.The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended user.With the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target domain.The existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia content.Since energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network lifetime.With this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for WMSN.The proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum computing.The proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance measures.The simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

N-Body Simulation Inspired by Metaheuristics Optimization

The N-body problem in classical physics, is the calculation of force ofgravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists andmathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been themajor hurdles in solving this problem for large and complex galaxies. Adventof high performance computational machines have mitigated the problem to muchextent, but still for large number of bodies it consumes huge amount of resourcesand days for computation. Conventional algorithms have been able to reduce thecomputational complexity from O n2 ð Þ to O nlogn ð Þ by splitting the space into atree or mesh network, researchers are still looking for improvements. In thisresearch work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. Theresults are compared with that of conventional algorithms. The outcomes showabout 50% time saving with almost no loss in accuracy. The proposed approachbeing a metaheuristics optimization technique, attempts to find optimal solution tothe problem, searching the entire space in a unique and efficient manner in a verylimited amount of time.

MIP Formulations and Metaheuristics for Multi-Item Capacitated Lot-Sizing Problem with Non-Customer Specific Production Time Windows and Setup Times

Our research focuses on the development of two cooperative approaches for resolution of the multi-item capacitated lot-sizing problems with time windows and setup times (MICLSP-TW-ST). In this paper we combine variable neighborhood search and accurate mixed integer programming (VNS-MIP) to solve MICLSP-TW-ST. It concerns so a particularly important and difficult problem in production planning. This problem is NP-hard in the strong sense. Moreover, it is very difficult to solve with an exact method;it is for that reason we have made use of the approximate methods. We improved the variable neighborhood search (VNS) algorithm, which is efficient for solving hard combinatorial optimization problems. This problem can be viewed as an optimization problem with mixed variables (binary variables and real variables). The new VNS algorithm was tested against 540 benchmark problems. The performance of most of our approaches was satisfactory and performed better than the algorithms already proposed in the literature.

Rapid and Flexible User-Defined Low-Level Hybridization for Metaheuristics Algorithm in Software Framework

The metaheuristics algorithm is increasingly important in solving many kinds of real-life optimization problems but the implementation involves programming difficulties. As a result, many researchers have relied on software framework to accelerate the development life cycle. However, the available software frameworks were mostly designed for rapid development rather than flexible programming. Therefore, in order to extend software functions, this approach involves modifying software libraries which requires the programmers to have in-depth understanding about the internal working structure of software and the programming language. Besides, it has restricted programmers for implementing flexible user-defined low-level hybridization. This paper presents the concepts and formal definition of metaheuristics and its low-level hybridization. In addition, the weaknesses of current programming approaches supported by available software frameworks for metaheuristics are discussed. Responding to the deficiencies, this paper introduces a rapid and flexible software framework with scripting language environment. This approach is more flexible for programmers to create a variety of user-defined low-level hybridization rather than bounded with built-in metaheuristics strategy in software libraries.

Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging effects.That is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical industry.In this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain Kd.The controller is used to control Mobile Robot System(MRS)at the required set point.The FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system response.The FF is touted as providing an easy,reliable,and efficient tuning technique for PID controllers.The most suitable ideal performance is accomplished with FF-PID,according to the display in the time response.Further,the observed response is compared to those received by applying GA and conventional off-line tuning techniques.The comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

A survey on the Metaheuristics for Cryptanalysis of Substitution and Transposition Ciphers

This paper presents state-of-art cryptanalysis studies on attacks of the substitution and transposition ciphers using various metaheuristic algorithms.Traditional cryptanalysis methods employ an exhaustive search,which is computationally expensive.Therefore,metaheuristics have attracted the interest of researchers in the cryptanalysis field.Metaheuristic algorithms are known for improving the search for the optimum solution and include Genetic Algorithm,Simulated Annealing,Tabu Search,Particle Swarm Optimization,Differential Evolution,Ant Colony,the Artificial Bee Colony,Cuckoo Search,and Firefly algorithms.The most important part of these various applications is deciding the fitness function to guide the search.This review presents how these algorithms have been implemented for cryptanalysis purposes.The paper highlights the results and findings of the studies and determines the gaps in the literature.

On the Metaheuristics Approach to the Problem of Genetic Sequence Comparison and Its Parallel Implementation

We describe parallel implementation of the metaheuristic approach to the problem of comparing strings representing DNA sequence. By this approach, one can define a whole new class of metrics on a set of strings;some of this metrics can lead to interesting results when used for string comparison. We propose several heuristics;compare results achieved when using those heuristics and compare parallel and sequential implementation of proposed approach.

Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,online forums,or gaming platforms.Cyberbullying involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and cyberstalking.Unfortunately,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this issue.Therefore,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this purpose.Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not.Additionally,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by metaheuristics.For this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost model.Additionally,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was performed.Finally,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.