A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Systematic Review:Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

Algorithms for Pre-Compiling Programs by Parallel Compilers

The paper addresses the challenge of transmitting a big number offiles stored in a data center(DC),encrypting them by compilers,and sending them through a network at an acceptable time.Face to the big number offiles,only one compiler may not be sufficient to encrypt data in an acceptable time.In this paper,we consider the problem of several compilers and the objective is tofind an algorithm that can give an efficient schedule for the givenfiles to be compiled by the compilers.The main objective of the work is to minimize the gap in the total size of assignedfiles between compilers.This minimization ensures the fair distribution offiles to different compilers.This problem is considered to be a very hard problem.This paper presents two research axes.Thefirst axis is related to architecture.We propose a novel pre-compiler architecture in this context.The second axis is algorithmic development.We develop six algorithms to solve the problem,in this context.These algorithms are based on the dispatching rules method,decomposition method,and an iterative approach.These algorithms give approximate solutions for the studied problem.An experimental result is imple-mented to show the performance of algorithms.Several indicators are used to measure the performance of the proposed algorithms.In addition,five classes are proposed to test the algorithms with a total of 2350 instances.A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm.The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic(ISL)with a percentage equal to 97.7%and an average running time equal to 0.148 s.All other algorithms did not exceed 22%as a percentage.The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic(ILS)with a percentage equal to 21,4%and an average running time equal to 0.150 s.

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well.Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world.However,with the advancement of technology,the Internet of Things(IoT)and social IoT(SIoT),the versatile data produced by smart devices helped a lot in overcoming this lethal disease.Data mining is a technique that could be used for extracting useful information from massive data.In this study,we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset“COVID-19 Symptoms and Presence.”RapidMiner Studio ML software was used to apply the Decision Tree(DT),Random Forest(RF),K-Nearest Neighbors(K-NNs)and Naive Bayes(NB),Integrated Decision Tree(ID3)algorithms.To develop the model,the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures,Cohan’s kappa statistics,properly or mistakenly categorized cases and root means square error.The results demonstrate that DT outperforms other methods,with an accuracy of 98.42%and a root mean square error of 0.11.In the future,a devisedmodel will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets.

Generating of Test Data by Harmony Search Against Genetic Algorithms

Many search-based algorithms have been successfully applied in sev-eral software engineering activities.Genetic algorithms(GAs)are the most used in the scientific domains by scholars to solve software testing problems.They imi-tate the theory of natural selection and evolution.The harmony search algorithm(HSA)is one of the most recent search algorithms in the last years.It imitates the behavior of a musician tofind the best harmony.Scholars have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains.The test data generation process represents a critical task in software validation.Unfortunately,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process.This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed offinding the required test data.The current research performs an empirical comparison of the HSA and the GAs,and then the significance of the results is estimated using the t-Test.The study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the significance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing criterion.The results showed that the harmony search algorithm is significantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026<α(αis the significance level=0.05 at 95%confidence level).In contrast,there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of thefitness values is 0.25>α.

Training Neuro-Fuzzy by Using Meta-Heuristic Algorithms for MPPT

It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)recently.Traditional or soft computing methods are used for MPPT.Since soft computing approaches are more effective than traditional approaches,studies on MPPT have shifted in this direction.This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT.The meta-heuristic training algorithms used are particle swarm optimization(PSO),harmony search(HS),cuckoo search(CS),artificial bee colony(ABC)algorithm,bee algorithm(BA),differential evolution(DE)and flower pollination algorithm(FPA).The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms.The data of a 250 W photovoltaic(PV)is used in the applications.For effective MPPT,different neuro-fuzzy structures,different membership functions and different control parameter values are evaluated in detail.Related training algorithms are compared in terms of solution quality and convergence speed.The strengths and weaknesses of these algorithms are revealed.It is seen that the type and number of membership function,colony size,number of generations affect the solution quality and convergence speed of the training algorithms.As a result,it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.

Algorithms for automatic measurement of SIS-type hysteretic underdamped Josephson junction’s parameters by current-voltage characteristics

Some electrical parameters of the SIS-type hysteretic underdamped Josephson junction(JJ)can be measured by its current-voltage characteristics(IVCs).Currents and voltages at JJ are commensurate with the intrinsic noise level of measuring instruments.This leads to the need for multiple measurements with subsequent statistical processing.In this paper,the digital algorithms are proposed for the automatic measurement of the JJ parameters by IVC.These algorithms make it possible to implement multiple measurements and check these JJ parameters in an automatic mode with the required accuracy.The complete sufficient statistics are used to minimize the root-mean-square error of parameter measurement.A sequence of current pulses with slow rising and falling edges is used to drive JJ,and synchronous current and voltage readings at JJ are used to realize measurement algorithms.The algorithm performance is estimated through computer simulations.The significant advantage of the proposed algorithms is the independence from current source noise and intrinsic noise of current and voltage meters,as well as the simple implementation in automatic digital measuring systems.The proposed algorithms can be used to control JJ parameters during mass production of superconducting integrated circuits,which will improve the production efficiency and product quality.

Nonlinear Analysis of Structures by Total Potential Optimization Using Metaheuristic Algorithms (TPO/MA)

Structural analysis problems can be formulized as either root finding problems,or optimization problems.The general practice is to choose the first option directly or to convert the second option again to a root finding problem by taking relevant derivatives and equating them to zero.The second alternative is used very randomly as it is and only for some simple demonstrative problems,most probably due to difficulty in solving optimization problems by classical methods.The method called TPO/MA(Total Potential Optimization using Metaheuristic Algorithms)described in this study successfully enables to handle structural problems with optimization formulation.Using metaheuristic algorithms provides additional advantages in dealing with all kinds of constraints.

Data-Driven Learning Control Algorithms for Unachievable Tracking Problems

For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

Intelligent Solution System for Cloud Security Based on Equity Distribution:Model and Algorithms

In the cloud environment,ensuring a high level of data security is in high demand.Data planning storage optimization is part of the whole security process in the cloud environment.It enables data security by avoiding the risk of data loss and data overlapping.The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient.In our work,we propose a data scheduling model for the cloud environment.Themodel is made up of three parts that together help dispatch user data flow to the appropriate cloudVMs.The first component is the Collector Agent whichmust periodically collect information on the state of the network links.The second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the scheduler.The third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable paths.It should be noted that each part of the proposedmodel requires the development of its algorithms.In this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link state.These algorithms are based on the grouping of transmitted files and the iterative method.The proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)problem.The experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their goods.Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years.The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones.This paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other things.Online reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are dynamic.Then,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave Bayes.The performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics.

Decoupling Algorithms for the Gravitational Wave Spacecraft

The gravitational wave spacecraft is a complex multi-input multi-output dynamic system.The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precision control.Establishing one spacecraftwith two laser links,compared to one spacecraft with a single laser link,requires an upgraded decoupling algorithmfor the link establishment.The decoupling algorithmwe designed reassigns the degrees of freedomand forces in the control loop to ensure sufficient degrees of freedomfor optical axis control.In addressing the distinct dynamic characteristics of different degrees of freedom,a transfer function compensation method is used in the decoupling process to further minimize motion coupling.The open-loop frequency response of the systemis obtained through simulation.The upgraded decoupling algorithms effectively reduce the open-loop frequency response by 30 dB.The transfer function compensation method efficiently suppresses the coupling of low-frequency noise.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management.In this study,Tuna Swarm Optimization(TSO),Whale Optimization Algorithm(WOA),and Cuckoo Search(CS)were used to optimize two hyperparameters in support vector regression(SVR).Based on these methods,three hybrid models to predict peak particle velocity(PPV)for bench blasting were developed.Eighty-eight samples were collected to establish the PPV database,eight initial blasting parameters were chosen as input parameters for the predictionmodel,and the PPV was the output parameter.As predictive performance evaluation indicators,the coefficient of determination(R2),rootmean square error(RMSE),mean absolute error(MAE),and a10-index were selected.The normalizedmutual information value is then used to evaluate the impact of various input parameters on the PPV prediction outcomes.According to the research findings,TSO,WOA,and CS can all enhance the predictive performance of the SVR model.The TSO-SVR model provides the most accurate predictions.The performances of the optimized hybrid SVR models are superior to the unoptimized traditional prediction model.The maximum charge per delay impacts the PPV prediction value the most.

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.