• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

    2024-01-12 03:46:04ShanshanWangQuanYuanWeiweiTanTengfeiYangandLiangZeng
    Computers Materials&Continua 2023年12期

    Shanshan Wang ,Quan Yuan ,Weiwei Tan ,Tengfei Yang and Liang Zeng,?

    1School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan,430068,China

    2Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Hubei University of Technology,Wuhan,430068,China

    3Xiangyang Industrial Institute of Hubei University of Technology,Xiangyang,441100,China

    ABSTRACT Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy.However,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search process.Therefore,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection problem.In this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm (SCA) and the Chimp Optimization Algorithm (ChOA),enabling a more effective search in the objective space.Secondly,an S-shaped transfer function is introduced to perform binary transformation on SCChOA.Finally,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection method.To evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are considered.Meanwhile,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for comparison.Experimental results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection problem.It is capable of maximizing the reduction in the number of selected features while maintaining a high classification accuracy.Furthermore,the results of statistical tests also confirm the significant effectiveness of this method.

    KEYWORDS Metaheuristics;chimp optimization algorithm;sine-cosine algorithm;feature selection and classification

    1 Introduction

    In various domains,such as machine learning and data mining,datasets frequently consist of a multitude of features.However,it is important to note that not all of these features are relevant or beneficial for the specific learning task at hand.Irrelevant features can negatively impact the model’s performance.Additionally,as datasets grow,the dimensionality of the data also increases,resulting in higher demands on the efficiency of model training and prediction[1].Consequently,Feature Selection(FS) plays a crucial role in identifying the most pertinent and valuable features from the original dataset [2].This process reduces data dimensionality,improves model accuracy and generalization,and reduces computational costs.Due to its numerous benefits,FS finds wide-ranging applications in various fields[3].As a result,it has gained significant attention as a vital research area in recent years.

    In general,FS methods can be categorized into three categories based on their relationship with the learning algorithm:filter approaches,wrapper approaches,and embedded approaches.Filter approaches are considered to be the fastest FS methods as they do not require training models and have lower computational costs[4].However,in many cases,filter approaches may not identify the optimal feature subset[5].On the other hand,wrapper approaches consider FS and the learning algorithm as a whole.They iteratively train the learning algorithm with different feature subsets to choose the best subset for training the model.However,the quality of FS by wrapper methods is dependent on the classifier,which results in wrapper methods getting better classification accuracy but being slower[6].Additionally,embedded approaches integrate FS with the training process of the learning algorithm[7].They adapt the features during the training process to select the features that contribute the most to the performance of the learning algorithm.Embedded approaches have relatively weaker modeling performance compared to wrapper methods,but they offer better computational efficiency[8].

    In order to improve FS,search methods for FS are continually evolving.Traditional search methods like Sequential Forward/Backward Selection (SFS/SBS) were formerly popular [9].Yet,these methods possess several limitations,such as issues with hierarchy and high computational costs.Consequently,Floating FS methods such as Sequential Forward/Backward Floating Selection(SFFS/SBFS) were proposed as alternatives [10].However,with the generation of large-scale highdimensional datasets,floating search techniques may not necessarily yield the optimal solution.

    In recent years,Metaheuristic Algorithms(MAs)have gained significant popularity for solving a wide range of optimization and FS problems.These algorithms have demonstrated success in quickly finding the closest solutions,without the need for computing gradients or relying on specific problem characteristics[11].This inherent flexibility has contributed to their widespread adoption.MAs can be categorized into four main types:Evolutionary Algorithms(EAs),Swarm Intelligence Algorithms(SIs),Physics-Based Algorithms(PAs),and Human-Inspired Algorithms(HAs).EAs are inspired by biological processes and simulate the process of natural evolution.One of the most commonly used EAs is the Genetic Algorithm (GA) [12],which is based on Darwin’s theory of evolution.SIs are inspired by collective intelligence behavior.Examples of SIs include Particle Swarm Optimization(PSO) [13],Ant Colony Optimization (ACO) [14],and Whale Optimization Algorithm (WOA) [15].Recently,some interesting SIs have been proposed,such as Beluga Whale Optimization (BWO) [16]and Artificial Rabbits Optimization (ARO) [17].PAs are based on physical principles and motion laws.Examples include Simulated Annealing(SA)[18],Equilibrium Optimizer(EO)[19].HAs mimic human behavior and interaction.Examples include Teaching-Learning-Based Optimization(TLBO)[20]and Imperialist Competitive Algorithm(ICA)[21],which are frequently cited techniques.

    The Chimp Optimization Algorithm(ChOA)is a performance efficient SI algorithm proposed in 2020 by Khishe et al.[22].This algorithm is inspired by the individual intelligence,sexual motivation,and predatory behavior of chimps.It effectively replicates chimps’driving,chasing,and attacking patterns to develop an efficient optimization scheme.In recent years,the ChOA algorithm and its variations have been successfully applied to various engineering problems,including gear transmission design [23],multi-layer perceptron training [24],and the order reduction problem of Said-Ball curves[25].

    The Sine Cosine Algorithm(SCA)is a PA method developed in 2016[26].By imitating the sine and cosine functions’oscillation,which mimics the motion of waves in nature,the SCA looks for the best solution.As a result,it offers the advantages of fast convergence and easy implementation,and it finds extensive application across diverse domains for addressing optimization challenges.

    While the ChOA algorithm exhibits good performance in solving specific problems,it faces challenges such as slow convergence speed and a tendency to get trapped in local optima when dealing with complex optimization problems[24].Further research indicates that these limitations stem from ChOA’s insufficient exploration capability.To tackle this issue,the present paper introduces a novel approach that combines the ChOA with SCA.This proposed method synergistically combines the exploration and exploitation capabilities of both algorithms by utilizing SCA to guide the ChOA for enhanced exploration in the search space.On one hand,the exploration capability mainly comes from SCA,and on the other hand,the exploitation part is handled by ChOA.The decision to combine the ChOA and SCA is primarily motivated by the simplicity and effectiveness of the ChOA,as well as the unique sine-cosine search capability of the SCA.The objective of combining these two heuristics is to develop a hybrid algorithm that is simpler and more efficient for feature selection.The main contributions of this paper are as follows:

    ? Proposing a novel hybrid sine-cosine chimp optimization algorithm for feature selection.By combining the chimp optimization algorithm with the sine-cosine algorithm,the unique characteristics of both algorithms are effectively utilized.

    ? Evaluating,classifying,and validating the efficiency of the selected feature subsets obtained from the hybrid algorithm using the KNN classifier.

    ? Comparing the proposed hybrid feature selection method with seven advanced feature selection methods on 16 datasets using well-known evaluation metrics such as average fitness value,average classification accuracy,average number of selected features,and average runtime.

    ? In addition,the Wilcoxon rank-sum test is conducted to examine the significant differences between the results obtained from the proposed hybrid feature selection technique and the compared methods.

    The paper is structured as follows:Section 2 presents a comprehensive review of previous related work.Section 3 provides a detailed description of the proposed feature selection (FS) method.Section 4 explains the experimental setup and presents the analysis and results of the conducted experiments.Finally,Section 5 discusses the conclusions drawn from the study.

    2 Literature Review

    In recent years,there has been a growing trend among researchers to utilize MAs in order to tackle a diverse array of FS problems.Among these algorithms,GA has gained popularity due to its effectiveness in optimization problems.Yang et al.were pioneers in using GA to solve FS problems[27].Additionally,Kennedy et al.suggested the BPSO[28],a variant of the PSO,which is particularly well-suited for binary optimization problems.Afterward,several variants of PSO emerged,such as a three-phase hybrid FS algorithm based on correlation-guided clustering and PSO[29],bare-bones PSO with mutual information[30],and multiobjective PSO with fuzzy cost[31].These variants have achieved remarkable results in the field of feature selection.More recently,Mafarja et al.introduced a binary version of WOA specifically for FS and classification tasks[32].Moreover,a novel FS method based on the Marine Predators Algorithm was developed for three coronavirus disease(COVID-19)datasets[33].This demonstrates the increasing demand for innovative optimization methods and their subsequent impact on the development of new FS techniques tailored to specific challenges.

    By combining the strengths of various MAs,it is possible to strike a balance between exploration and exploitation,effectively mitigating the limitations associated with individual algorithms.As a result,hybrid algorithms have received increasing attention in FS problems.For example,Al-Tashi et al.presented a discrete version of hybrid PSO and GWO,named BGWOPSO [34].The experimental results demonstrated that BGWOPSO outperformed other methods in terms of both accuracy and cost time.Similarly,Ling et al.proposed the NL-BGWOA[35]for FS,which combined WOA and GOA to optimize the diversity in search.The results showed that this method had a high accuracy of up to 0.9895 and superiority in solving FS problems on medical datasets.Recently,a hybrid FS method that combined the Dipper Throated and Grey Wolf Optimization(DTO-GW)was proposed[36].This method utilized binary DTO-GW to identify the best subset of the aim dataset.A comparative analysis,conducted on 8 life benchmark datasets,demonstrated the superior performance of this method in solving the FS problem.In order to enhance the classification model’s overall performance,researchers proposed two Stages of Local Search models for FS [37].The two models were based on the WOA and the Great Deluge (GD).The effectiveness of the proposed models in searching the feature space and improving classification performance was evaluated using 15 standard datasets.Moreover,a novel wrapper feature selection method called BWPLFS was introduced,which combines the WOA,PSO,and Lévy Flight [38].Experimental results demonstrated that BWPLFS selects the most effective features,showing promise for integration with decision support systems to enhance accuracy and efficiency.In order to improve the accuracy of cancer classification and the efficiency of gene selection,researchers proposed a novel gene selection strategy called BCOOTCSA [39],which combined the binary COOT optimization algorithm with simulated annealing.Experimental results demonstrated that BCOOT-CSA outperformed other techniques in terms of prediction accuracy and the number of selected genes,making it a promising approach for cancer classification.Therefore,it is crucial to carefully select appropriate hybrid algorithms based on specific problem characteristics and conduct thorough experimental evaluations to validate their performance.Furthermore,ongoing research and development efforts should focus on advancing the techniques and methodologies of hybrid algorithms to further enhance their capabilities in FS and optimization tasks.

    According to the No Free Lunch theorem [40],no optimization algorithm can solve all optimization problems,whether past,present,or future.While algorithms may perform well on specific datasets,their performance may decline when applied to similar or different types of datasets.Although the methods mentioned in the literature each have their own characteristics,none of them can address all FS issues.Therefore,it is essential to improve existing methods or propose novel approaches to enhance the resolution of FS problems.Following is a discussion of a hybrid wrapperbased method for selection features.

    3 Methodology

    The proposed method is a hybrid algorithm that combines the Chimp Optimization Algorithm and the Sine Cosine Algorithm.In this section,the fundamental knowledge of the proposed method will be explained,as well as a demonstration and discussion of the proposed method.

    3.1 Chimp Optimization Algorithm(ChOA)

    ChOA is a novel intelligent algorithm,proposed by Khishe et al.in 2020,which is based on chimp hunting behavior.Based on the behavior of group division of labor and cooperation,ChOA classifies the leaders of chimp groups into four types: attacker,barrier,chaser,and driver.In this scenario,the attacker is the role of the leader,and the others assist,with their level decreasing in order.The mathematical models of chimp driving,obstructing,and chasing prey are described as below:

    here,d(t)represents the distance between the prey and the chimp at the current iterationt.The chaotic vectormis generated by chaotic maps.Xprey(t) is the position vector of the prey,andXchimp(t+1) is the position vector of the chimp.The coefficient vectorsaandcare represented by Eqs.(3)and(4),respectively.

    wherer1andr2are random numbers between 0 and 1.The parameterfis a decreasing factor that nonlinearly decreases from 2.5 to 0 with increasing iteration number.Therefore,the parameteratakes values between-fandf.Whenais within the range of-1 to 1,the chimp launches an attack on the prey,thus ending the hunting process.Otherwise,the next position of the chimp can be arbitrarily selected from all chimp positions.Thus,the mathematical model of chimp attacking the prey is described by Eqs.(5)–(7).

    whereX(t)represents the current position of the chimp agent,Xa(t),Xb(t),Xc(t)andXd(t)represent the positions of the current attacker,barrier,chaser,and driver,respectively,andda(t),db(t),dc(t)anddd(t) represent the distance vectors between the corresponding chimp and the current chimp agent.The next position of the chimp individual is randomly distributed within a circle determined by the positions of these top four individuals.In other words,the positions of the other chimps are guided by the positions of them.

    In the final stage of hunting,when the chimps are satisfied with the prey,they are driven by social motivation to release their nature.At this point,the chimps will try to obtain food in a forced and chaotic way.Six deterministic chaotic maps[22]are used to describe this social behavior,with a 50% probability of choosing either the conventional position update way or the chaotic model.The mathematical representation of social motivation behavior is shown in Eq.(8).

    whereChaotic_valueis a chaotic map.

    3.2 Sine Cosine Algorithm(SCA)

    SCA is an efficient MA based on sine and cosine laws.The optimization process of SCA involves two phases:exploration and exploitation,which are balanced by the sine and cosine functions.During the exploration phase,the algorithm searches for promising areas with high randomness in a large search space,while during the exploitation phase,it performs local search near previously explored points.For both phases,the position of the candidate solution in SCA is updated using the following Eq.(9):

    wherearepresents a constant,which is taken as 2.

    3.3 The Proposed Hybrid SCChOA Algorithm

    In this section,we introduce the proposed hybrid SCChOA algorithm.The proposed hybrid method combines the ChOA with the SCA.While the ChOA algorithm is effective for simple optimization problems,it tends to struggle with complex problems,such as high-dimensional feature selection,because it often gets trapped in local optimal solutions rather than finding the global.This is primarily attributed to the limited exploration capability of ChOA in handling complex tasks.On the other hand,the SCA utilizes unique sine and cosine waves for spatial exploration and offers advantages in terms of high convergence accuracy and strong exploration capability.Therefore,this improvement aims to enhance the exploration capability of ChOA by incorporating SCA as the local search component.Specifically,we propose integrating the SCA operator into the attack process of chimps to address the limitations of the standard ChOA version.

    In the SCA,the value ofr2in the sine and cosine formulas is randomly chosen from the range 0 to 2π[26],resulting in sine and cosine values ranging from-1 to 1.However,the pseudo-random nature ofr2leads to non-uniform and unpredictable values.This can result in extreme situations,such as very small values for sine or cosine in the early iterations,and very large values later on.In this case,the SCA exhibits a weak search capability in early phases and poor exploitation capability later.Therefore,in order to better combine the SCA operator with ChOA,we propose a multi-cycle iteration strategy to address the shortcomings caused by this pseudo-randomness.This strategy is achieved by designing multi-cycle iteration factorλ,and its mathematical model is as follows:

    wherekrepresents the number of cycles,and different values can be chosen based on the characteristics of the feature space.In this paper,kis determined to be 16.Therefore,the mathematical model for the position update of ChOA combined with the SCA operator is as follows:

    By embedding the SCA operator during the chimp attack phase,the individual chimps in the proposed algorithm exhibit stronger search capabilities compared to the original ChOA algorithm.This is mainly attributed to the SCA operator providing a wider search space for the chimps through cosine and sine oscillations,enabling them to overlook local optima and quickly capture global optima.Specifically,when a chimp individual is satisfied with its current food source(local optima),the SCA operator drives it to explore the vicinity of the current solution with cosine and sine oscillations.This is effective for all four leaders among the chimps,and other chimps update their positions based on the locations of these four leaders.Additionally,the introduction of the multi-cycle factor ensures that during the iteration process,the search range becomes more specific,allowing individuals to continue exploring nearby small spaces while maintaining their cosine and sine oscillation states.As a result,the proposed algorithm can not only perform global search through chimp attacks but also conduct more precise local search with the probing abilities of SCA,thereby better discovering global optimal solutions.Algorithm 1 presents the pseudocode for SCChOA.

    3.4 The Proposed Feature Selection Method

    In this section,we introduce the proposed feature selection method.A binary process is involved in feature selection,which relies on whether a particular feature is chosen to solve a problem or not.In order for the hybrid SCChOA algorithm to be applicable for feature selection,it needs to be converted into binary format.Subsequently,the classifier KNN is combined with the binary SCChOA algorithm to form a binary hybrid wrapper feature selection algorithm.The resulting optimal solution is converted to binary 0 or 1 to select the best subset.Typically,a sigmoid function is employed for this conversion,as depicted in the following equation,whereXbestrepresents the optimal position at iteration numbert.

    The quality of the candidate solutions obtained by the proposed algorithm is evaluated using a fitness function.The fitness function is designed to minimize the size of the selected feature subset and maximize the classification accuracy of the selected learning algorithm[32].Its calculation method is as follows:

    Theαandβare parameters that control the contribution weights of selecting the feature subset size and the classification accuracy of the selected learning algorithm.The sum of their weights is 1.errrepresents the classification error of the classifier used,andRrepresents the number of selected features out of the total number of features(Num)in the dataset.

    Hence,the flowchart of the proposed feature selection method is shown as Fig.1.

    4 Experimental Results

    In this section,we discussed the experimental setup and presented and discussed the experimental results.

    4.1 Description of the Datasets

    To analyze the performance of SCChOA,we conducted experiments using 16 standard UCI datasets [41].These datasets are sourced from various domains,which demonstrates the versatility of the proposed method.Table 1 presents fundamental details about the datasets.The inclusion of datasets with varying numbers of features and instances enables us to assess the effectiveness of the proposed approach.

    Table 1:16 standard UCI datasets

    Figure 1:The flowchart of proposed feature selection method

    4.2 Experimental Configurations

    The proposed SCChOA method is compared with seven advanced metaheuristic methods mentioned in the literature.These methods include BPSO [28],BChOA [22],BSCA [26],BHHO [42],BWOA [32],CCSA [43],and BGWOPSO [34].The parameters of the comparison algorithms can be found in Table 2.To ensure a fair evaluation,the population size and number of iterations are consistently set to 20 and 100,respectively.To assess the quality of the generated solutions,the KNN classifier is used as the wrapper framework,configured withK=5.To further enhance the reliability of the results,k-fold cross-validation withk=10 is employed to train and test the classifier.For each optimizer,twenty independent runs are conducted to account for variability.The simulation experiments are performed on a computer equipped with an Intel(R) Core(TM) i5-7200U CPU operating at a frequency of 2.50 GHz and 12 GB of memory.Meanwhile,MATLAB 2019b is utilized as the software platform for conducting the experiments.

    Table 2:The configuration parameters of different methods

    4.3 Evaluation Criteria

    In this paper,four well-known metrics are used to evaluate the proposed method.These four metrics are as follows:

    (1)Average fitness value:The average fitness value represents the average of the fitness values over all the runs.

    heretmaxdenotes the maximum number of runs andFitidenotes the value of the best fitness of thei-th individual.The calculation ofFitican be referred to as Eq.(19).

    (2)Average classification accuracy:The average classification accuracy represents the average of the classification accuracies over all the runs.

    whereAccirepresents the classification accuracy of the classifier in thei-th iteration.

    (3)Average feature selection number: The average number of selected features represents the average of the number of selected features over all the runs.

    wherelen(BSi) represents the number of selected features in the best solution of thei-th feature selection.The smaller the value ofFN,the more capable the algorithm is in reducing the number of features in the dataset.

    (4)Average running time(seconds):The average running time is calculated by taking the average of the running times of all the runs.

    here,timeirepresents the running time of thei-th feature selection run.

    4.4 Evaluation Results

    In this section,we compare and analyze the proposed method and other methods based on the four metrics mentioned above.Moreover,we conduct a detailed analysis of the results and perform statistical analysis using the Wilcoxon’s rank-sum test.

    Table 3 displays the average fitness values of SCChOA and other competing methods for each selected dataset.It is evident that SCChOA achieves the best average fitness values for 14 datasets.While CCSA and BGWOPSO obtain the best fitness values for DS5 and DS10,respectively,their performance on other datasets is generally poor.In summary,SCChOA exhibits the lowest overall average fitness value of 0.1313 across all methods and datasets.Furthermore,Fig.2 illustrates that SCChOA has the best Friedman average fitness ranking at 1.47,indicating its competitiveness in minimizing the fitness value.In other words,the SCChOA can effectively streamline the number of features to be selected while minimizing the classification error.

    Figure 2:The friedman average fitness value ranking

    Table 4 presents a comparison of the SCChOA method with other methods in terms of classification accuracy.Among the 16 datasets analyzed,SCChOA achieves the highest classification accuracy in 14 of them.In contrast,the BGWOPSO and CCSA methods only obtain the highest classification accuracy in 2 datasets,while others obtain worse classification accuracy.Notably,all methods achieve 100%classification accuracy on dataset DS7,which can be attributed to the smaller number of individuals in that dataset,making the classification task less challenging.However,as the number of features and instances increases,the performance of most algorithms tends to decline.When considering more complex high-dimensional datasets,it is evident that SCChOA consistently maintains a high level of classification accuracy,surpassing other algorithms and securing the top ranking in classification accuracy for multiple high-dimensional datasets.This underscores the strong competitiveness of SCChOA in addressing complex high-dimensional feature selection classification problems.Upon evaluating the classification accuracy results from the 16 datasets,the average classification accuracy for all algorithms is computed.SCChOA achieves the highest average classification accuracy of 0.8767.The following three algorithms,namely BPSO,BPSOGWO,and BSCA,closely follow with average classification accuracies of 0.8598,0.8574,and 0.8519,respectively.Furthermore,the Friedman average ranking of classification accuracy as Fig.3 reveals that SCChOA has an average ranking of 1.47,placing it in the first position.This further emphasizes the effectiveness of SCChOA in feature selection for classification tasks.

    Table 4:The average classification accuracy

    Figure 3:The Friedman average classification accuracy ranking

    In terms of the number of selected features,Table 5 presents a comparison of different methods on 16 selected UCI datasets.From the results in Table 5,it can be observed that the proposed algorithm has an average number of selected features of 19.6 and ranks second among the evaluated algorithms.Additionally,SCChOA achieved the minimum number of selected features in 5 datasets,which is not the best among the methods.BChOA and BSCA achieved the minimum number of selected features in 7 and 8 datasets,respectively.However,both of these methods did not perform well in terms of classification accuracy and fitness value ranking.This indicates that the primary objective of feature selection is to ensure higher classification accuracy,followed by reducing the number of features.Based on this observation,SCChOA demonstrates strong competitiveness in solving feature selection problems as it is able to maintain high classification accuracy while effectively reducing the number of features.

    Table 5:The average feature selection number

    On the other hand,Table 6 presents a comparison of the actual running times of different methods on all datasets.It can be observed that SCChOA shows comparable average time costs to BSCA and BChOA,without exhibiting higher time expenses.Overall,in comparison to other methods,SCChOA also demonstrates a relatively faster running speed compared to the majority of the compared algorithms.This suggests that SCChOA can achieve satisfactory performance in feature selection while also offering certain advantages in terms of time costs.

    Table 6:The average running times

    Based on the aforementioned four indicators,the proposed method outperforms all other compared methods in terms of average fitness value and average classification accuracy,which are the two most important indicators.Moreover,the proposed method also exhibits certain advantages in terms of average number of the selected features and the average runtime.These results can be attributed to the embedded SCA operator,which provides additional search possibilities for individual gorillas during the search process.In situations where a chimp individual becomes trapped in a local optimum,the SCA operator assists in escaping from this local value,enabling the chimp to further explore superior solutions by avoiding complacency with the current food source.Furthermore,the proposed hybrid algorithm showcases a similar average runtime compared to the original ChOA and SCA without incurring any additional time overhead.This is due to the fact that the embedded SCA operator does not introduce any additional time complexity,thereby ensuring simplicity and efficiency in the algorithm.

    To provide a more intuitive demonstration of SCChOA’s effectiveness in tackling the feature selection problem,Fig.4 shows a visualized comparison showcasing the objective function fitness values obtained by various methods across a set of representative datasets.Notably,SCChOA consistently achieves the best results when compared to other methods,thereby emphasizing its superiority and effectiveness in addressing the feature selection problem.

    Figure 4:The visualized comparison of fitness values

    To determine the statistical significance of the previously obtained results,a Wilcoxon’s rank-sum test is conducted on the experimental data.The significance level chosen for the test is 5%.The results of the test are displayed in Table 7.This particular test evaluates the hypothesis for two independent samples and produces ap-value as the outcome.The null hypothesis states that there is no significant difference between the two samples,and if thep-value is greater than 0.05,it raises doubts about the validity of the null hypothesis.The statistical test results reveal that for almost all datasets,thep-values are below 5%.Overall,the performance of SCChOA demonstrates significant distinctions when compared to the other seven algorithms,suggesting that SCChOA is more effective than the other comparison methods.

    Table 7:Wilcoxon’s rank-sum test results

    5 Conclusions

    This paper presents the SCChOA,a novel hybrid algorithm for feature selection problems.This method combines the characteristics of the SCA and ChOA to effectively address feature selection challenges.In order to assess the performance of the proposed method,it is evaluated on 16 UCI datasets using four evaluation metrics:average fitness value,average classification accuracy,average feature selection number,and average running time.The SCChOA is compared with seven stateof-the-art metaheuristic-based feature selection methods,including BPSO,BWOA,BSCA,BHHO,BChOA,BGWOPSO,and CCSA.The results indicate that SCChOA achieves the best results in terms of average fitness value and average classification accuracy.The average fitness value and average classification accuracy are 0.1313 and 0.8767,respectively.Furthermore,this method exhibits satisfactory performance with regards to average feature selection count and average running time.These results demonstrate the high competitiveness of SCChOA in addressing feature selection problems.Additionally,statistical tests confirmed the algorithm’s significant effectiveness.

    In our future research,we aim to explore the potential of SCChOA in feature selection problems across diverse domains,such as surface defect classification in industrial steel belts and feature selection in real medical datasets like breast cancer datasets.This exploration holds the promise of improving quality control processes in industrial environments and contributing to disease diagnosis and treatment in the medical field.Additionally,investigating the potential of SCChOA in workshop scheduling and wind power prediction is also a promising direction.These research endeavors are expected to uncover practical applications of SCChOA in various domains or problems.

    Acknowledgement:The authors wish to acknowledge the editor and anonymous reviewers for their insightful comments,which have improved the quality of this publication.

    Funding Statement:This work was in part supported by the Key Research and Development Project of Hubei Province(No.2023BAB094),the Key Project of Science and Technology Research Program of Hubei Educational Committee (No.D20211402),and the Teaching Research Project of Hubei University of Technology(No.2020099).

    Author Contributions:Study conception and design: Shanshan Wang,Quan Yuan;Data collection:Weiwei Tan,Tengfei Yang and Liang Zeng;Analysis and interpretation of results:all authors;Draft manuscript preparation:Quan Yuan.All authors reviewed the results and approved the final version of the manuscript.

    Availability of Data and Materials:Data and materials are available in UCI machine learning repository.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    久久午夜综合久久蜜桃| 在线观看国产h片| 99精国产麻豆久久婷婷| 国产欧美日韩一区二区三区在线| 国产男人的电影天堂91| 肉色欧美久久久久久久蜜桃| 欧美亚洲日本最大视频资源| 一级毛片电影观看| 国产成人午夜福利电影在线观看| 美女福利国产在线| 菩萨蛮人人尽说江南好唐韦庄| 最近中文字幕2019免费版| tube8黄色片| 久久 成人 亚洲| 在线观看免费日韩欧美大片| 国产成人午夜福利电影在线观看| 亚洲精品第二区| 国产一区二区 视频在线| 国产精品一国产av| 人人妻人人爽人人添夜夜欢视频| 狠狠精品人妻久久久久久综合| xxxhd国产人妻xxx| 国产黄频视频在线观看| 巨乳人妻的诱惑在线观看| 在现免费观看毛片| 国产成人精品婷婷| 午夜日本视频在线| 亚洲在久久综合| 乱人伦中国视频| 777久久人妻少妇嫩草av网站| 久久久精品94久久精品| 午夜日本视频在线| 99国产精品免费福利视频| 成年av动漫网址| 黄色视频在线播放观看不卡| 亚洲国产精品一区三区| 国产精品亚洲av一区麻豆 | 国产精品.久久久| 日韩电影二区| 国产乱人偷精品视频| 三级国产精品片| 国产1区2区3区精品| 精品一区二区三区四区五区乱码 | 亚洲国产av新网站| 一级片免费观看大全| 波野结衣二区三区在线| 少妇被粗大的猛进出69影院| 男女午夜视频在线观看| 国产精品久久久久久精品古装| 不卡av一区二区三区| 综合色丁香网| 97在线视频观看| 中文字幕色久视频| 日本av免费视频播放| 亚洲欧美一区二区三区黑人 | 制服人妻中文乱码| 婷婷色综合大香蕉| 国产在线视频一区二区| 韩国精品一区二区三区| 国产老妇伦熟女老妇高清| 国产视频首页在线观看| 91aial.com中文字幕在线观看| 精品人妻熟女毛片av久久网站| 男人舔女人的私密视频| 国产深夜福利视频在线观看| 亚洲情色 制服丝袜| 中文天堂在线官网| 18禁国产床啪视频网站| 国产深夜福利视频在线观看| 水蜜桃什么品种好| 久久久久人妻精品一区果冻| 日日撸夜夜添| 纵有疾风起免费观看全集完整版| 伦精品一区二区三区| 精品亚洲乱码少妇综合久久| 久久人人爽人人片av| 美女福利国产在线| 亚洲精品国产一区二区精华液| 亚洲美女黄色视频免费看| 亚洲av日韩在线播放| 国语对白做爰xxxⅹ性视频网站| 国产激情久久老熟女| 国产综合精华液| 色视频在线一区二区三区| 宅男免费午夜| 国产乱来视频区| 嫩草影院入口| 人人澡人人妻人| 国产免费一区二区三区四区乱码| 久久久久久伊人网av| 久久免费观看电影| 色94色欧美一区二区| 欧美黄色片欧美黄色片| 亚洲国产毛片av蜜桃av| 妹子高潮喷水视频| 最近手机中文字幕大全| 精品第一国产精品| 日韩av免费高清视频| 少妇人妻 视频| 2022亚洲国产成人精品| 岛国毛片在线播放| 赤兔流量卡办理| 免费少妇av软件| 欧美亚洲日本最大视频资源| 9热在线视频观看99| 9191精品国产免费久久| 天美传媒精品一区二区| 日韩大片免费观看网站| 哪个播放器可以免费观看大片| 交换朋友夫妻互换小说| 中文字幕另类日韩欧美亚洲嫩草| 亚洲欧美成人精品一区二区| 亚洲精品中文字幕在线视频| a 毛片基地| 国产免费一区二区三区四区乱码| 国产人伦9x9x在线观看 | 国产精品嫩草影院av在线观看| 亚洲 欧美一区二区三区| 亚洲欧洲日产国产| 亚洲欧美成人综合另类久久久| 日韩人妻精品一区2区三区| 性高湖久久久久久久久免费观看| 亚洲精品一二三| 亚洲欧美色中文字幕在线| 老汉色∧v一级毛片| 曰老女人黄片| 最近手机中文字幕大全| 丝袜美足系列| 久久97久久精品| 可以免费在线观看a视频的电影网站 | 亚洲欧美一区二区三区国产| 精品少妇久久久久久888优播| 日韩成人av中文字幕在线观看| 亚洲av福利一区| 精品久久久久久电影网| 欧美精品一区二区免费开放| 精品福利永久在线观看| 成年美女黄网站色视频大全免费| 波野结衣二区三区在线| 十八禁网站网址无遮挡| 日韩在线高清观看一区二区三区| 久久久久久久亚洲中文字幕| 97在线人人人人妻| 妹子高潮喷水视频| 女性被躁到高潮视频| 人妻人人澡人人爽人人| 国产精品久久久久久av不卡| 国产日韩一区二区三区精品不卡| 午夜日韩欧美国产| 亚洲成色77777| tube8黄色片| 伊人久久大香线蕉亚洲五| 久久精品久久久久久久性| 亚洲三区欧美一区| 国产片特级美女逼逼视频| 亚洲国产最新在线播放| av天堂久久9| 激情五月婷婷亚洲| 男女高潮啪啪啪动态图| 久久久久久久精品精品| 成人漫画全彩无遮挡| 观看美女的网站| 人人妻人人添人人爽欧美一区卜| 亚洲婷婷狠狠爱综合网| 一本久久精品| 欧美日韩精品网址| 日韩 亚洲 欧美在线| 国产日韩欧美视频二区| 妹子高潮喷水视频| 黄色 视频免费看| 十八禁高潮呻吟视频| 欧美在线黄色| 精品国产一区二区三区四区第35| 久久精品熟女亚洲av麻豆精品| h视频一区二区三区| 欧美+日韩+精品| 90打野战视频偷拍视频| 人体艺术视频欧美日本| 国产高清不卡午夜福利| 边亲边吃奶的免费视频| 午夜日本视频在线| 亚洲av中文av极速乱| 男女啪啪激烈高潮av片| 国产黄色免费在线视频| 1024视频免费在线观看| 超色免费av| www.熟女人妻精品国产| 搡女人真爽免费视频火全软件| 日本wwww免费看| 亚洲欧美成人综合另类久久久| 国产精品秋霞免费鲁丝片| 国产国语露脸激情在线看| 街头女战士在线观看网站| 国产精品国产三级专区第一集| 久久99精品国语久久久| 欧美精品一区二区免费开放| 久久99精品国语久久久| 国产精品无大码| 两性夫妻黄色片| 国产成人精品久久二区二区91 | 欧美人与性动交α欧美软件| 夜夜骑夜夜射夜夜干| 亚洲精品日本国产第一区| 成年人午夜在线观看视频| 亚洲一区中文字幕在线| 777久久人妻少妇嫩草av网站| 久久久久精品性色| 99久久精品国产国产毛片| 国语对白做爰xxxⅹ性视频网站| 91精品伊人久久大香线蕉| 人妻人人澡人人爽人人| a级毛片黄视频| a级毛片黄视频| 九色亚洲精品在线播放| 老司机影院成人| a 毛片基地| 老司机影院成人| 国产精品免费视频内射| 国产成人aa在线观看| 人妻系列 视频| 婷婷色av中文字幕| 久久精品国产亚洲av天美| 亚洲三级黄色毛片| 国产成人午夜福利电影在线观看| 国产精品不卡视频一区二区| 亚洲精品美女久久av网站| 在线天堂中文资源库| 免费高清在线观看日韩| 一级毛片黄色毛片免费观看视频| 黄色配什么色好看| 看免费成人av毛片| 国产免费现黄频在线看| 亚洲人成77777在线视频| 久久精品人人爽人人爽视色| 国产熟女欧美一区二区| 国产av精品麻豆| 国产高清不卡午夜福利| 日韩人妻精品一区2区三区| 91国产中文字幕| 少妇人妻久久综合中文| 一本色道久久久久久精品综合| 一级片'在线观看视频| 国产成人免费无遮挡视频| 亚洲四区av| 日韩一区二区三区影片| videos熟女内射| 亚洲精品一二三| 狠狠婷婷综合久久久久久88av| 国产欧美日韩综合在线一区二区| 久久久久精品久久久久真实原创| 七月丁香在线播放| 国产高清国产精品国产三级| 黄色毛片三级朝国网站| 国产片特级美女逼逼视频| 国产精品蜜桃在线观看| 国产色婷婷99| 精品人妻熟女毛片av久久网站| 亚洲第一区二区三区不卡| 青草久久国产| 精品国产一区二区久久| 一边亲一边摸免费视频| av国产精品久久久久影院| 99久国产av精品国产电影| 女的被弄到高潮叫床怎么办| 精品国产一区二区三区四区第35| 人人妻人人爽人人添夜夜欢视频| 成年美女黄网站色视频大全免费| 成人黄色视频免费在线看| 亚洲综合色网址| 好男人视频免费观看在线| 国产亚洲一区二区精品| 亚洲国产毛片av蜜桃av| 亚洲人成网站在线观看播放| 久久女婷五月综合色啪小说| 男女啪啪激烈高潮av片| 日日撸夜夜添| 青春草亚洲视频在线观看| 中文字幕精品免费在线观看视频| 亚洲精品国产av成人精品| 一区二区三区激情视频| 国产精品久久久久久精品古装| 欧美97在线视频| 国产一区二区激情短视频 | 欧美97在线视频| 精品国产一区二区三区四区第35| 亚洲男人天堂网一区| 国产熟女欧美一区二区| 亚洲色图 男人天堂 中文字幕| 国产亚洲最大av| 日日摸夜夜添夜夜爱| 亚洲五月色婷婷综合| 香蕉精品网在线| 欧美97在线视频| 两个人免费观看高清视频| 性高湖久久久久久久久免费观看| 精品福利永久在线观看| 精品一区二区三区四区五区乱码 | 午夜日韩欧美国产| 国产精品久久久久久精品电影小说| 三上悠亚av全集在线观看| 18禁观看日本| 国产精品蜜桃在线观看| 日韩成人av中文字幕在线观看| 亚洲久久久国产精品| 久久热在线av| 波野结衣二区三区在线| 国产男女超爽视频在线观看| 日本-黄色视频高清免费观看| 两性夫妻黄色片| 国产亚洲午夜精品一区二区久久| 亚洲综合精品二区| 啦啦啦啦在线视频资源| 国产色婷婷99| 国产成人精品福利久久| 美女大奶头黄色视频| 成人国语在线视频| 国产免费视频播放在线视频| 日韩中文字幕欧美一区二区 | 丝袜人妻中文字幕| 美女国产视频在线观看| 色哟哟·www| 我要看黄色一级片免费的| 亚洲国产欧美日韩在线播放| 亚洲国产毛片av蜜桃av| videosex国产| 91国产中文字幕| 有码 亚洲区| 欧美国产精品va在线观看不卡| 成人二区视频| 国产成人午夜福利电影在线观看| 最近最新中文字幕大全免费视频 | 2021少妇久久久久久久久久久| 亚洲国产欧美日韩在线播放| 免费看不卡的av| 亚洲精品久久久久久婷婷小说| 啦啦啦在线观看免费高清www| 亚洲,一卡二卡三卡| 人妻系列 视频| 国产xxxxx性猛交| 看十八女毛片水多多多| 亚洲激情五月婷婷啪啪| 久久久久视频综合| 免费观看无遮挡的男女| 建设人人有责人人尽责人人享有的| 欧美av亚洲av综合av国产av | 久久国产精品男人的天堂亚洲| 大香蕉久久网| av免费观看日本| 亚洲精品国产色婷婷电影| 欧美 亚洲 国产 日韩一| 极品少妇高潮喷水抽搐| 在线天堂中文资源库| 欧美xxⅹ黑人| 多毛熟女@视频| 午夜91福利影院| 色婷婷久久久亚洲欧美| 韩国av在线不卡| 午夜免费观看性视频| 国产成人精品在线电影| 免费观看a级毛片全部| 亚洲内射少妇av| 99久久综合免费| 精品国产超薄肉色丝袜足j| 欧美精品高潮呻吟av久久| 老汉色av国产亚洲站长工具| 午夜日本视频在线| 免费av中文字幕在线| 亚洲精品成人av观看孕妇| 赤兔流量卡办理| 男女啪啪激烈高潮av片| 男女国产视频网站| 九色亚洲精品在线播放| 成年动漫av网址| 美女脱内裤让男人舔精品视频| 日本wwww免费看| 午夜日韩欧美国产| 中文字幕亚洲精品专区| 国产在线视频一区二区| 欧美精品一区二区免费开放| 黑人猛操日本美女一级片| 国产日韩欧美视频二区| 伦精品一区二区三区| 国产 一区精品| av国产精品久久久久影院| 在线观看www视频免费| 考比视频在线观看| 亚洲av中文av极速乱| 亚洲欧洲精品一区二区精品久久久 | 午夜免费男女啪啪视频观看| 一级片免费观看大全| av一本久久久久| 少妇 在线观看| 午夜福利一区二区在线看| 亚洲欧美精品自产自拍| 久久久久久伊人网av| 99久久精品国产国产毛片| 精品一区二区三卡| 永久免费av网站大全| av一本久久久久| 久久人人爽人人片av| 欧美变态另类bdsm刘玥| 婷婷色麻豆天堂久久| 色哟哟·www| 咕卡用的链子| 精品亚洲成a人片在线观看| 在线天堂最新版资源| 亚洲一级一片aⅴ在线观看| 好男人视频免费观看在线| 国产深夜福利视频在线观看| 精品酒店卫生间| 69精品国产乱码久久久| 久久人人爽av亚洲精品天堂| 国产精品久久久久久精品古装| 久久鲁丝午夜福利片| 精品国产一区二区久久| 丰满少妇做爰视频| av在线老鸭窝| 天天操日日干夜夜撸| 久久综合国产亚洲精品| 欧美成人午夜免费资源| 久久人妻熟女aⅴ| 国产人伦9x9x在线观看 | 国产精品av久久久久免费| 菩萨蛮人人尽说江南好唐韦庄| 中文字幕精品免费在线观看视频| 一区福利在线观看| 日本av免费视频播放| 午夜福利视频精品| 丝袜喷水一区| 日韩人妻精品一区2区三区| 综合色丁香网| 新久久久久国产一级毛片| 免费女性裸体啪啪无遮挡网站| 啦啦啦在线观看免费高清www| www日本在线高清视频| 日韩精品免费视频一区二区三区| freevideosex欧美| 亚洲国产欧美网| 成人午夜精彩视频在线观看| 亚洲精品自拍成人| 久久精品久久精品一区二区三区| 免费黄网站久久成人精品| 永久免费av网站大全| 激情五月婷婷亚洲| 人妻系列 视频| 99国产精品免费福利视频| 欧美bdsm另类| 丰满少妇做爰视频| 午夜福利乱码中文字幕| 久久99精品国语久久久| 午夜福利视频精品| 国产淫语在线视频| 91国产中文字幕| 啦啦啦在线免费观看视频4| 久久精品国产鲁丝片午夜精品| 热re99久久精品国产66热6| 国产xxxxx性猛交| 如日韩欧美国产精品一区二区三区| 一本久久精品| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 国产一区二区在线观看av| 曰老女人黄片| 夫妻性生交免费视频一级片| 又粗又硬又长又爽又黄的视频| 韩国高清视频一区二区三区| 精品国产一区二区三区久久久樱花| 男女边吃奶边做爰视频| 三上悠亚av全集在线观看| 最近最新中文字幕大全免费视频 | 久热这里只有精品99| 日本午夜av视频| 最近2019中文字幕mv第一页| 成人毛片60女人毛片免费| 国产免费视频播放在线视频| 精品亚洲成国产av| 久久精品亚洲av国产电影网| 丰满少妇做爰视频| 欧美老熟妇乱子伦牲交| 99热网站在线观看| 日韩一区二区三区影片| 欧美日韩综合久久久久久| 国产精品三级大全| 国产爽快片一区二区三区| 天天操日日干夜夜撸| 午夜福利视频精品| 国产又色又爽无遮挡免| 日本wwww免费看| 少妇人妻精品综合一区二区| 老司机影院毛片| 久久久久久久久免费视频了| 亚洲欧美色中文字幕在线| 如日韩欧美国产精品一区二区三区| 欧美人与性动交α欧美软件| 久久精品国产亚洲av涩爱| 国产精品免费大片| 国产在线一区二区三区精| 大香蕉久久成人网| 久久久久网色| 色94色欧美一区二区| 激情五月婷婷亚洲| 久久久国产欧美日韩av| 国产 一区精品| 日韩 亚洲 欧美在线| 人体艺术视频欧美日本| 欧美日韩视频高清一区二区三区二| 国产日韩欧美在线精品| 欧美日韩视频精品一区| 亚洲五月色婷婷综合| 中文字幕人妻丝袜制服| 国产成人免费无遮挡视频| 国产又色又爽无遮挡免| 大码成人一级视频| 熟妇人妻不卡中文字幕| 亚洲av国产av综合av卡| 午夜福利影视在线免费观看| 日韩,欧美,国产一区二区三区| 97在线视频观看| tube8黄色片| 国产国语露脸激情在线看| 超碰97精品在线观看| 国产女主播在线喷水免费视频网站| 99九九在线精品视频| 欧美日本中文国产一区发布| 黑人猛操日本美女一级片| 国产精品久久久av美女十八| 亚洲欧洲国产日韩| 在线免费观看不下载黄p国产| 夫妻性生交免费视频一级片| 我的亚洲天堂| 久久久欧美国产精品| 自拍欧美九色日韩亚洲蝌蚪91| 中文字幕亚洲精品专区| 一区二区三区精品91| 国产精品久久久久久精品电影小说| 黑丝袜美女国产一区| 人人澡人人妻人| 少妇被粗大的猛进出69影院| 中文精品一卡2卡3卡4更新| 97在线人人人人妻| 久久久久精品久久久久真实原创| 天堂俺去俺来也www色官网| 人成视频在线观看免费观看| 99久久精品国产国产毛片| 老司机影院毛片| 亚洲,一卡二卡三卡| 欧美+日韩+精品| 欧美精品av麻豆av| 久久婷婷青草| 免费不卡的大黄色大毛片视频在线观看| 免费黄网站久久成人精品| 制服诱惑二区| 精品国产超薄肉色丝袜足j| a级毛片在线看网站| 五月天丁香电影| 午夜福利在线免费观看网站| 最近中文字幕高清免费大全6| 91精品三级在线观看| 国产日韩一区二区三区精品不卡| 国产色婷婷99| 大码成人一级视频| 亚洲第一av免费看| 亚洲国产欧美在线一区| 久久久a久久爽久久v久久| 日韩大片免费观看网站| 丁香六月天网| 国产成人91sexporn| av在线app专区| 性少妇av在线| 国产亚洲av片在线观看秒播厂| 国产成人91sexporn| 国产深夜福利视频在线观看| 成人国产麻豆网| 18禁观看日本| 亚洲美女视频黄频| 老熟女久久久| 精品一区二区免费观看| 韩国精品一区二区三区| 久久久精品国产亚洲av高清涩受| 久久人人97超碰香蕉20202| 日本午夜av视频| 国产欧美日韩一区二区三区在线| 久久精品国产自在天天线| 女人被躁到高潮嗷嗷叫费观| 看非洲黑人一级黄片| 纯流量卡能插随身wifi吗| 国产国语露脸激情在线看| 18禁观看日本| 久久这里只有精品19| 国产日韩欧美亚洲二区| 99久久中文字幕三级久久日本| av在线老鸭窝| 午夜激情久久久久久久| 日韩欧美一区视频在线观看| 国产亚洲欧美精品永久| 精品人妻在线不人妻| 国产视频首页在线观看| 人人妻人人爽人人添夜夜欢视频| 在线观看美女被高潮喷水网站| 在线精品无人区一区二区三| 一区二区三区四区激情视频| av有码第一页| 好男人视频免费观看在线| 国产av码专区亚洲av| 老司机亚洲免费影院| 美女大奶头黄色视频| 在线免费观看不下载黄p国产| 青草久久国产| 纯流量卡能插随身wifi吗| 国产精品秋霞免费鲁丝片| 国产亚洲av片在线观看秒播厂| 中文字幕色久视频| 日韩一区二区视频免费看| 国产精品一区二区在线不卡| 亚洲成av片中文字幕在线观看 | 最近的中文字幕免费完整|