Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.T...Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.展开更多
Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,wh...Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,which combines an enhanced Bat Algorithm(BA)and Support Vector Machine(SVM).To enhance the optimization capability of BA,water follow search and random follow search are introduced to optimize the efficiency and accuracy of the feature subset search.Experimental validation conducted on the IEEE CEC 2017 benchmark functions and the talented innovative capacity dataset demonstrates the efficacy of the proposed method relative to peer and prominent machine learning models.The experimental results reveal that the predictive accuracy of the bWRBA-SVM-FS model is 97.503%,with a sensitivity of 98.391%.Our findings indicate significant predictors of innovation capacity,including project application goals,educational background,and interdisciplinary thinking abilities.The bWRBA-SVM-FS model offers effective strategies for talent selection in higher education,fostering the development of future research leaders.展开更多
Pulmonary embolism(PE)can range from minor,asymptomatic blood clots to life-threatening emboli capable of obstructing pulmonary arteries,potentially leading to cardiac arrest and fatal outcomes.Due to this significant...Pulmonary embolism(PE)can range from minor,asymptomatic blood clots to life-threatening emboli capable of obstructing pulmonary arteries,potentially leading to cardiac arrest and fatal outcomes.Due to this significant mortality risk,risk stratification is essential following PE diagnosis to guide appropriate therapeutic intervention.This study proposes a machine learning-based methodology for PE risk stratification,utilizing clinical data from a cohort of 139 patients.The predictive framework integrates an enhanced binary Honey Badger Algorithm(BCCHBA)with the K-Nearest Neighbor(KNN)classifier.To comprehensively evaluate the performance of the core optimization algorithm(CCHBA),a series of benchmark function tests were conducted.Furthermore,diagnostic validation tests were performed using real-world PE patient data collected from medical facilities,demonstrating the clinical significance and practical utility of the BCCHBA-KNN system.Analysis revealed the critical importance of specific indicators,including neutrophil percentage(NEUT%),systolic blood pressure(SBP),oxygen saturation(SaO2%),white blood cell count(WBC),and syncope.The classification results demonstrated exceptional performance,with the prediction model achieving 100%sensitivity and 99.09%accuracy.This approach holds promise as a novel and accurate method for assessing PE severity.展开更多
Photovoltaic(PV)systems are electrical systems designed to convert solar energy into electrical energy.As a crucial component of PV systems,harsh weather conditions,photovoltaic panel temperature and solar irradiance ...Photovoltaic(PV)systems are electrical systems designed to convert solar energy into electrical energy.As a crucial component of PV systems,harsh weather conditions,photovoltaic panel temperature and solar irradiance influence the power output of photovoltaic cells.Therefore,accurately identifying the parameters of PV models is essential for simulating,controlling and evaluating PV systems.In this study,we propose an enhanced weighted-mean-of-vectors optimisation(EINFO)for efficiently determining the unknown parameters in PV systems.EINFO introduces a Lambert W-based explicit objective function for the PV model,enhancing the computational accuracy of the algorithm's population fitness.This addresses the challenge of improving the metaheuristic algorithms'identification accuracy for unknown parameter identification in PV models.We experimentally apply EINFO to three types of PV models(single-diode,double-diode and PV-module models)to validate its accuracy and stability in parameter identification.The results demonstrate that EINFO achieves root mean square errors(RMSEs)of 7.7301E-04,6.8553E-04 and 2.0608E-03 for the single-diode model,double-diode model and PV-module model,respectively,surpassing those obtained by using INFO algorithm as well as other methods in terms of convergence speed,accuracy and stability.Furthermore,comprehensive experimental findings on three commercial PV modules(ST40,SM55 and KC200GT)indicate that EINFO consistently maintains high accuracy across varying temperatures and irradiation levels.In conclusion,EINFO emerges as a highly competitive and practical approach for parameter identification in diverse types of PV models.展开更多
The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world problems.Specifically,it shows deficiencies in terms of ...The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world problems.Specifically,it shows deficiencies in terms of limited local exploration capabilities and less precise solutions.Therefore,this research aims to integrate the topological search(TS)mechanism with the gradient search rule(GSR)into the framework of RUN,introducing an enhanced algorithm called TGRUN to improve the performance of the original algorithm.The TS mechanism employs a circular topological scheme to conduct a thorough exploration of solution regions surrounding each solution,enabling a careful examination of valuable solution areas and enhancing the algorithm’s effectiveness in local exploration.To prevent the algorithm from becoming trapped in local optima,the GSR also integrates gradient descent principles to direct the algorithm in a wider investigation of the global solution space.This study conducted a serious of experiments on the IEEE CEC2017 comprehensive benchmark function to assess the enhanced effectiveness of TGRUN.Additionally,the evaluation includes real-world engineering design and feature selection problems serving as an additional test for assessing the optimisation capabilities of the algorithm.The validation outcomes indicate a significant improvement in the optimisation capabilities and solution accuracy of TGRUN.展开更多
The bat algorithm(BA)is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness,which can be used to find the globally opti...The bat algorithm(BA)is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness,which can be used to find the globally optimal solutions for various optimisation problems.Knowing the recent criticises of the originality of equations,the principle of BA is concise and easy to implement,and its mathematical structure can be seen as a hybrid particle swarm with simulated annealing.In this research,the authors focus on the performance optimisation of BA as a solver rather than discussing its originality issues.In terms of operation effect,BA has an acceptable convergence speed.However,due to the low proportion of time used to explore the search space,it is easy to converge prematurely and fall into the local optima.The authors propose an adaptive multi-stage bat algorithm(AMSBA).By tuning the algorithm's focus at three different stages of the search process,AMSBA can achieve a better balance between exploration and exploitation and improve its exploration ability by enhancing its performance in escaping local optima as well as maintaining a certain convergence speed.Therefore,AMSBA can achieve solutions with better quality.A convergence analysis was conducted to demonstrate the global convergence of AMSBA.The authors also perform simulation experiments on 30 benchmark functions from IEEE CEC 2017 as the objective functions and compare AMSBA with some original and improved swarm-based algorithms.The results verify the effectiveness and superiority of AMSBA.AMSBA is also compared with eight representative optimisation algorithms on 10 benchmark functions derived from IEEE CEC 2020,while this experiment is carried out on five different dimensions of the objective functions respectively.A balance and diversity analysis was performed on AMSBA to demonstrate its improvement over the original BA in terms of balance.AMSBA was also applied to the multi-threshold image segmentation of Citrus Macular disease,which is a bacterial infection that causes lesions on citrus trees.The segmentation results were analysed by comparing each comparative algorithm's peak signal-to-noise ratio,structural similarity index and feature similarity index.The results show that the proposed BA-based algorithm has apparent advantages,and it can effectively segment the disease spots from citrus leaves when the segmentation threshold is at a low level.Based on a comprehensive study,the authors think the proposed optimiser has mitigated the main drawbacks of the BA,and it can be utilised as an effective optimisation tool.展开更多
The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and stron...The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.展开更多
The 100-kernel weight of corn seed is a crucial metric for assessing corn quality,and the current measurement means mostly involve manual counting of kernels followed by weighing on a balance,which is labour-intensive...The 100-kernel weight of corn seed is a crucial metric for assessing corn quality,and the current measurement means mostly involve manual counting of kernels followed by weighing on a balance,which is labour-intensive and time-consuming.Aiming to address the problem of low efficiency in measuring the 100-kernel weight of corn seeds,this study proposes a measurement method based on deep learning and machine vision.In this study,high-contrast camera technology was utilised to capture image data of corn seeds.And improvements were made to the feature extraction network of the YOLOv5 model by incorporating the MobileNetV3 network structure.The novel model employs deep separable convolution to decrease parameters and computational load.It incorporates a linear bottleneck and inverted residual structure to enhance efficiency.It introduces an SE attention mechanism for direct learning of channel number features and updates the activation function.Algorithms and experiments were subsequently designed to calculate the 100-grain weight in conjunction with the output of the model.The outcomes revealed that the enhanced model in this study achieved an accuracy of 90.1%,a recall rate of 91.3%,and a mAP(mean average precision)value of 92.2%.While meeting production requirements,this model significantly reduces the number of parameters compared to alternative models-50% of the original model.In an applied study focused on measuring the 100-kernel weight of corn seeds,the counting accuracy yielded a remarkable 97.18%,while the accuracy for weight measurement results reached 94.2%.This study achieves both efficient and precise measurement of the 100-kernel weight of maize seeds,presenting a novel perspective in the exploration of maize seed weight.展开更多
Objective: The present study is to investigate the expression of CD34, β-Tubulin-III and Collagen IV-Laminin in adenocarcinoma in situ (AIS), the AIS component of minimally invasive adenocarcinoma (MIA), and early in...Objective: The present study is to investigate the expression of CD34, β-Tubulin-III and Collagen IV-Laminin in adenocarcinoma in situ (AIS), the AIS component of minimally invasive adenocarcinoma (MIA), and early invasive foci, in order to find a valuable immunohistochemical marker for discriminating AIS and its early invasive foci. Methods: A total of 51 AIS patients and 88 MIA patients were included in the present study. In addition, 40 atypical adenomatous hyperplasia (AAH) patients and 54 invasive adenocarcinoma (IA) patients were included as control. Immunohisto-chemical staining of β-Tubulin-III, CD34, CD31, F8 and Collagen IV-Laminin was performed by serial sectioning. β-Tubulin-III was used to show invasive adenocarcinoma foci, CD34 was used to indicate interstitial cells in AIS, CD31 and F8 were used to identify capillary endothelial cells in tumor tissues, and Collagen IV-Laminin was used to visualize the basement membrane component of AIS. Results: The basement membranes and interstitial cells of AAH, AIS and the AIS component of MIA had positive expression of CD34, while mucinous AIS and various invasive adenocarcinomas had no CD34-positive basement membranes or interstitial cells. Invasive cancers such as alveolar adenocarcinoma, papillary adenocarcinoma, micropapillary adenocarcinoma and solid adenocarcinoma had strong positive expression of β-Tubulin-III, while AAH, AIS and the AIS component of MIA, and invasive mucinous adenocarcinoma had negative expression of β-Tubulin-III. AAH, AIS and the AIS component of MIA were surrounded by basement membranes with positive expression of Collagen IV-Laminin, AIS and the AIS component of MIA had significantly thickened basement membranes, and none of invasive adenocarcinomas was surrounded by basement membranes. Conclusions: The present study demonstrates that immunohistochemical staining of CD34, β-Tubulin-III, and Collagen IV-Laminin discriminates AIS component of lung adenocarcinoma from early invasive foci, with the efficacy of β-Tubulin-III being the best. Staining of β-Tubulin-III precisely identifies the early invasive foci of MIA, and can be used as a marker for the identification of the early invasive foci of nonmucinous lung adenocarcinoma.展开更多
This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by anima...This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication.展开更多
As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually developed.T...As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually developed.The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance,which can effectively alleviate the disadvantages and problems of traditional covert communication.However,its combination with covert communication thus far has been mostly at the theoretical level.The BLOCCE method,as an early result of the combination of blockchain and covert communication technology,has the problems of low information embedding efficiency,the use of too many Bitcoin addresses,low communication efficiency,and high costs.The present research improved on this method,designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information.This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used.Under the premise of ensuring the order,the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency.Thus,a more efficient and feasible method for the application of covert communication on the blockchain is proposed.In addition,this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.展开更多
Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex problems.To address the shortcomings,an improved WOA(QGBWOA)is proposed in this work.First,quasi-o...Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex problems.To address the shortcomings,an improved WOA(QGBWOA)is proposed in this work.First,quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions.Second,a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA.To verify the advantages of QGBWOA,comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10,30,50,and 100 and on CEC 2020 test with dimension 30.Furthermore,the performance results were tested using Wilcoxon signed-rank(WS),Friedman test,and post hoc statistical tests for statistical analysis.Convergence accuracy and speed are remarkably improved,as shown by experimental results.Finally,feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems.QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.展开更多
The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional data.Feature Selection(FS)methods can abate the complexity of the data and enhance the accuracy,g...The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional data.Feature Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of models.Meta-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient performance.This paper introduces an augmented Forensic-Based Investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the FBI.Moreover,a binary version of DCFBI(BDCFBI)is applied to FS.Experiments conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search capability.The influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied dimensions.BDCFBI is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary metaheuristics.The results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.展开更多
Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopa...Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images.This method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS algorithm.The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset.In addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images.Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution.According to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation experiments.Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.展开更多
Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The orig...Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The original version of the CSA has simple parameters and moderate performance.However,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases.Therefore,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization problems.To verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions.The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum.In addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection problems.Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems.The proposed CCMSCSA performs well in almost all experimental results.Therefore,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.展开更多
Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the...Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the combined economic emission dispatch(CEED)problem.Inspired by the foraging behavior in the slime mould algorithm(SMA),the kernel matrix of the kernel search optimizer(KSO)is intensified.The proposed BKSO is superior to the standard KSO in terms of exploitation ability,robustness,and convergence rate.The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms.BKSO performs better in statistical results and convergence curves.At the same time,BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases,and the Pareto solution obtained is also better than other MAs.Based on the experimental results,BKSO has better performance than other comparable MAs and can provide more economical,robust,and cleaner solutions to CEED problems.展开更多
Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidem...Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.展开更多
Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it under...Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.展开更多
The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning t...The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.展开更多
文摘Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.
基金supported by the Zhejiang Province 14th Five Year Plan Teaching Reform Project(jg20220514).
文摘Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,which combines an enhanced Bat Algorithm(BA)and Support Vector Machine(SVM).To enhance the optimization capability of BA,water follow search and random follow search are introduced to optimize the efficiency and accuracy of the feature subset search.Experimental validation conducted on the IEEE CEC 2017 benchmark functions and the talented innovative capacity dataset demonstrates the efficacy of the proposed method relative to peer and prominent machine learning models.The experimental results reveal that the predictive accuracy of the bWRBA-SVM-FS model is 97.503%,with a sensitivity of 98.391%.Our findings indicate significant predictors of innovation capacity,including project application goals,educational background,and interdisciplinary thinking abilities.The bWRBA-SVM-FS model offers effective strategies for talent selection in higher education,fostering the development of future research leaders.
文摘Pulmonary embolism(PE)can range from minor,asymptomatic blood clots to life-threatening emboli capable of obstructing pulmonary arteries,potentially leading to cardiac arrest and fatal outcomes.Due to this significant mortality risk,risk stratification is essential following PE diagnosis to guide appropriate therapeutic intervention.This study proposes a machine learning-based methodology for PE risk stratification,utilizing clinical data from a cohort of 139 patients.The predictive framework integrates an enhanced binary Honey Badger Algorithm(BCCHBA)with the K-Nearest Neighbor(KNN)classifier.To comprehensively evaluate the performance of the core optimization algorithm(CCHBA),a series of benchmark function tests were conducted.Furthermore,diagnostic validation tests were performed using real-world PE patient data collected from medical facilities,demonstrating the clinical significance and practical utility of the BCCHBA-KNN system.Analysis revealed the critical importance of specific indicators,including neutrophil percentage(NEUT%),systolic blood pressure(SBP),oxygen saturation(SaO2%),white blood cell count(WBC),and syncope.The classification results demonstrated exceptional performance,with the prediction model achieving 100%sensitivity and 99.09%accuracy.This approach holds promise as a novel and accurate method for assessing PE severity.
基金partially supported by MRC(MC_PC_17171)Royal Society(RP202G0230)+8 种基金BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(P202PF11)Sino-UK Industrial Fund(RP202G0289)Sino-UK Education Fund(OP202006)LIAS(P202ED10,P202RE969)Data Science Enhancement Fund(P202RE237)Fight for Sight(24NN201)BBSRC(RM32G0178B8).
文摘Photovoltaic(PV)systems are electrical systems designed to convert solar energy into electrical energy.As a crucial component of PV systems,harsh weather conditions,photovoltaic panel temperature and solar irradiance influence the power output of photovoltaic cells.Therefore,accurately identifying the parameters of PV models is essential for simulating,controlling and evaluating PV systems.In this study,we propose an enhanced weighted-mean-of-vectors optimisation(EINFO)for efficiently determining the unknown parameters in PV systems.EINFO introduces a Lambert W-based explicit objective function for the PV model,enhancing the computational accuracy of the algorithm's population fitness.This addresses the challenge of improving the metaheuristic algorithms'identification accuracy for unknown parameter identification in PV models.We experimentally apply EINFO to three types of PV models(single-diode,double-diode and PV-module models)to validate its accuracy and stability in parameter identification.The results demonstrate that EINFO achieves root mean square errors(RMSEs)of 7.7301E-04,6.8553E-04 and 2.0608E-03 for the single-diode model,double-diode model and PV-module model,respectively,surpassing those obtained by using INFO algorithm as well as other methods in terms of convergence speed,accuracy and stability.Furthermore,comprehensive experimental findings on three commercial PV modules(ST40,SM55 and KC200GT)indicate that EINFO consistently maintains high accuracy across varying temperatures and irradiation levels.In conclusion,EINFO emerges as a highly competitive and practical approach for parameter identification in diverse types of PV models.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Numbers:LTGS23E070001,LZ22F020005,LTGY24C060004National Natural Science Foundation of China,Grant/Award Numbers:62076185,62301367,62273263。
文摘The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world problems.Specifically,it shows deficiencies in terms of limited local exploration capabilities and less precise solutions.Therefore,this research aims to integrate the topological search(TS)mechanism with the gradient search rule(GSR)into the framework of RUN,introducing an enhanced algorithm called TGRUN to improve the performance of the original algorithm.The TS mechanism employs a circular topological scheme to conduct a thorough exploration of solution regions surrounding each solution,enabling a careful examination of valuable solution areas and enhancing the algorithm’s effectiveness in local exploration.To prevent the algorithm from becoming trapped in local optima,the GSR also integrates gradient descent principles to direct the algorithm in a wider investigation of the global solution space.This study conducted a serious of experiments on the IEEE CEC2017 comprehensive benchmark function to assess the enhanced effectiveness of TGRUN.Additionally,the evaluation includes real-world engineering design and feature selection problems serving as an additional test for assessing the optimisation capabilities of the algorithm.The validation outcomes indicate a significant improvement in the optimisation capabilities and solution accuracy of TGRUN.
基金BBSRC,Grant/Award Number:RM32G0178B8National Natural Science Foundation of China,Grant/Award Numbers:U19A2061,U1809209,62076185+11 种基金Science and Technology Development Project of Jilin Province,Grant/Award Number:20190301024NYJilin Provincial Industrial Innovation Special Fund Project,Grant/Award Number:2018C039-3MRC,Grant/Award Number:MC_PC_17171Royal Society,Grant/Award Number:RP202G0230BHF,Grant/Award Number:AA/18/3/34220Hope Foundation for Cancer Research,Grant/Award Number:RM60G0680GCRF,Grant/Award Number:P202PF11Sino-UK Industrial Fund,Grant/Award Number:RP202G0289LIAS,Grant/Award Numbers:P202ED10,P202RE969Data Science Enhancement Fund,Grant/Award Number:P202RE237Fight for Sight,Grant/Award Number:24NN201Sino-UK Education Fund,Grant/Award Number:OP202006。
文摘The bat algorithm(BA)is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness,which can be used to find the globally optimal solutions for various optimisation problems.Knowing the recent criticises of the originality of equations,the principle of BA is concise and easy to implement,and its mathematical structure can be seen as a hybrid particle swarm with simulated annealing.In this research,the authors focus on the performance optimisation of BA as a solver rather than discussing its originality issues.In terms of operation effect,BA has an acceptable convergence speed.However,due to the low proportion of time used to explore the search space,it is easy to converge prematurely and fall into the local optima.The authors propose an adaptive multi-stage bat algorithm(AMSBA).By tuning the algorithm's focus at three different stages of the search process,AMSBA can achieve a better balance between exploration and exploitation and improve its exploration ability by enhancing its performance in escaping local optima as well as maintaining a certain convergence speed.Therefore,AMSBA can achieve solutions with better quality.A convergence analysis was conducted to demonstrate the global convergence of AMSBA.The authors also perform simulation experiments on 30 benchmark functions from IEEE CEC 2017 as the objective functions and compare AMSBA with some original and improved swarm-based algorithms.The results verify the effectiveness and superiority of AMSBA.AMSBA is also compared with eight representative optimisation algorithms on 10 benchmark functions derived from IEEE CEC 2020,while this experiment is carried out on five different dimensions of the objective functions respectively.A balance and diversity analysis was performed on AMSBA to demonstrate its improvement over the original BA in terms of balance.AMSBA was also applied to the multi-threshold image segmentation of Citrus Macular disease,which is a bacterial infection that causes lesions on citrus trees.The segmentation results were analysed by comparing each comparative algorithm's peak signal-to-noise ratio,structural similarity index and feature similarity index.The results show that the proposed BA-based algorithm has apparent advantages,and it can effectively segment the disease spots from citrus leaves when the segmentation threshold is at a low level.Based on a comprehensive study,the authors think the proposed optimiser has mitigated the main drawbacks of the BA,and it can be utilised as an effective optimisation tool.
基金partially supported by MRC(MC_PC_17171)Royal Society(RP202G0230)+8 种基金BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(20P2PF11)Sino-UK Industrial Fund(RP202G0289)LIAS(20P2ED10,20P2RE969)Data Science Enhancement Fund(20P2RE237)Fight for Sight(24NN201)Sino-UK Education Fund(OP202006)BBSRC(RM32G0178B8).
文摘The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.
基金supported by the National Key R&D Program:Research on Key Technologies and Component Creation for Unmanned Land Finishing Operations(2022YFD200160202)the Jilin Provincial Science and Technology Development Program:Cloud Brain Technology and Platform for Unmanned Corn Operations(20220202032NC).
文摘The 100-kernel weight of corn seed is a crucial metric for assessing corn quality,and the current measurement means mostly involve manual counting of kernels followed by weighing on a balance,which is labour-intensive and time-consuming.Aiming to address the problem of low efficiency in measuring the 100-kernel weight of corn seeds,this study proposes a measurement method based on deep learning and machine vision.In this study,high-contrast camera technology was utilised to capture image data of corn seeds.And improvements were made to the feature extraction network of the YOLOv5 model by incorporating the MobileNetV3 network structure.The novel model employs deep separable convolution to decrease parameters and computational load.It incorporates a linear bottleneck and inverted residual structure to enhance efficiency.It introduces an SE attention mechanism for direct learning of channel number features and updates the activation function.Algorithms and experiments were subsequently designed to calculate the 100-grain weight in conjunction with the output of the model.The outcomes revealed that the enhanced model in this study achieved an accuracy of 90.1%,a recall rate of 91.3%,and a mAP(mean average precision)value of 92.2%.While meeting production requirements,this model significantly reduces the number of parameters compared to alternative models-50% of the original model.In an applied study focused on measuring the 100-kernel weight of corn seeds,the counting accuracy yielded a remarkable 97.18%,while the accuracy for weight measurement results reached 94.2%.This study achieves both efficient and precise measurement of the 100-kernel weight of maize seeds,presenting a novel perspective in the exploration of maize seed weight.
文摘Objective: The present study is to investigate the expression of CD34, β-Tubulin-III and Collagen IV-Laminin in adenocarcinoma in situ (AIS), the AIS component of minimally invasive adenocarcinoma (MIA), and early invasive foci, in order to find a valuable immunohistochemical marker for discriminating AIS and its early invasive foci. Methods: A total of 51 AIS patients and 88 MIA patients were included in the present study. In addition, 40 atypical adenomatous hyperplasia (AAH) patients and 54 invasive adenocarcinoma (IA) patients were included as control. Immunohisto-chemical staining of β-Tubulin-III, CD34, CD31, F8 and Collagen IV-Laminin was performed by serial sectioning. β-Tubulin-III was used to show invasive adenocarcinoma foci, CD34 was used to indicate interstitial cells in AIS, CD31 and F8 were used to identify capillary endothelial cells in tumor tissues, and Collagen IV-Laminin was used to visualize the basement membrane component of AIS. Results: The basement membranes and interstitial cells of AAH, AIS and the AIS component of MIA had positive expression of CD34, while mucinous AIS and various invasive adenocarcinomas had no CD34-positive basement membranes or interstitial cells. Invasive cancers such as alveolar adenocarcinoma, papillary adenocarcinoma, micropapillary adenocarcinoma and solid adenocarcinoma had strong positive expression of β-Tubulin-III, while AAH, AIS and the AIS component of MIA, and invasive mucinous adenocarcinoma had negative expression of β-Tubulin-III. AAH, AIS and the AIS component of MIA were surrounded by basement membranes with positive expression of Collagen IV-Laminin, AIS and the AIS component of MIA had significantly thickened basement membranes, and none of invasive adenocarcinomas was surrounded by basement membranes. Conclusions: The present study demonstrates that immunohistochemical staining of CD34, β-Tubulin-III, and Collagen IV-Laminin discriminates AIS component of lung adenocarcinoma from early invasive foci, with the efficacy of β-Tubulin-III being the best. Staining of β-Tubulin-III precisely identifies the early invasive foci of MIA, and can be used as a marker for the identification of the early invasive foci of nonmucinous lung adenocarcinoma.
基金This research was supported by the National Natural Science Foundation of China(62076185,U1809209).
文摘This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication.
基金This work is sponsored by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LC2016024Natural Science Foundation of the Jiangsu Higher Education Institutions Grant No.17KJB520044Six Talent Peaks Project in Jiangsu Province No.XYDXX-108.
文摘As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually developed.The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance,which can effectively alleviate the disadvantages and problems of traditional covert communication.However,its combination with covert communication thus far has been mostly at the theoretical level.The BLOCCE method,as an early result of the combination of blockchain and covert communication technology,has the problems of low information embedding efficiency,the use of too many Bitcoin addresses,low communication efficiency,and high costs.The present research improved on this method,designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information.This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used.Under the premise of ensuring the order,the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency.Thus,a more efficient and feasible method for the application of covert communication on the blockchain is proposed.In addition,this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.
基金the Zhejiang Provincial Natural Science Foundation of China(no.LZ21F020001)the Basic Scientific Research Program of Wenzhou(no.S20220018).
文摘Whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex problems.To address the shortcomings,an improved WOA(QGBWOA)is proposed in this work.First,quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions.Second,a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA.To verify the advantages of QGBWOA,comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10,30,50,and 100 and on CEC 2020 test with dimension 30.Furthermore,the performance results were tested using Wilcoxon signed-rank(WS),Friedman test,and post hoc statistical tests for statistical analysis.Convergence accuracy and speed are remarkably improved,as shown by experimental results.Finally,feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems.QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.
基金supported by Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(ZY20180119)the Natural Science Foundation of Zhejiang Province(LZ22F020005)+1 种基金the Natural Science Foundation of Hebei Province(D2022512001)National Natural Science Foundation of China(42164002,62076185).
文摘The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional data.Feature Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of models.Meta-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient performance.This paper introduces an augmented Forensic-Based Investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the FBI.Moreover,a binary version of DCFBI(BDCFBI)is applied to FS.Experiments conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search capability.The influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied dimensions.BDCFBI is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary metaheuristics.The results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LZ22F020005,LTGS23E070001)National Natural Science Foundation of China(62076185,U1809209).
文摘Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images.This method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS algorithm.The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset.In addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images.Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution.According to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation experiments.Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.
基金Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(42164002,62076185 and,U1809209)National Key R&D Program of China(2018YFC1503806).
文摘Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The original version of the CSA has simple parameters and moderate performance.However,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases.Therefore,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization problems.To verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions.The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum.In addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection problems.Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems.The proposed CCMSCSA performs well in almost all experimental results.Therefore,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.
基金This research was supported by the Science&Technology Development Project of Jilin Province,China(YDZJ202201ZYTS555)the Science&Technology Research Project of the Education Department of Jilin Province,China(JJKH20220244KJ)。
文摘Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the combined economic emission dispatch(CEED)problem.Inspired by the foraging behavior in the slime mould algorithm(SMA),the kernel matrix of the kernel search optimizer(KSO)is intensified.The proposed BKSO is superior to the standard KSO in terms of exploitation ability,robustness,and convergence rate.The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms.BKSO performs better in statistical results and convergence curves.At the same time,BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases,and the Pareto solution obtained is also better than other MAs.Based on the experimental results,BKSO has better performance than other comparable MAs and can provide more economical,robust,and cleaner solutions to CEED problems.
基金supported by the Natural Science Foundation of Zhejiang Province(LY21F020001,LZ22F020005)National Natural Science Foundation of China(62076185,U1809209)+1 种基金Science and Technology Plan Project of Wenzhou,China(ZG2020026)We also acknowledge the respected editor and reviewers'efforts to enhance the quality of this research.
文摘Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.
文摘Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.
基金supported by the National Natural Science Foundation of China(62076185,U1809209)supported by Zhejiang Provincial Natural Science Foundation of China(LY21F020030)+1 种基金Wenzhou Major Scientific and Technological Innovation Project(ZY2019019)Wenzhou Science and Technology Bureau(2018ZG016)。
文摘The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.
