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
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
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
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
Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems.To accurately and reliably extract the unknown parameters of ...Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems.To accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,INMVO.Quantitative experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and exploitation.Further,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for comparison.In addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results statistically.Various evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other techniques.In addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and temperatures.In conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV models.Note that the source code of INMVO is available at https://github.com/woniuzuioupao/INMVO.展开更多
In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more complex.Many novel and well-performing meta-heuristic algorithms have been studied and d...In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more complex.Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem.Among them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization performance.However,it tends to stagnate prematurely to local optima in solving some specific problems.Therefore,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called CGSE.In this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of SE.To objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with constraints.The final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real applications.Therefore,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.展开更多
This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenanc...This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency.RSWTLBO combines adaptive parameter w,Single Solution Optimization Mechanism(SSOM),and Weight Probability Exploration Strategy(WPES)to enhance the optimization ability of TLBO.The algorithm achieves a balance between exploitation and exploration throughout the iteration process.The SSOM allows for local exploration around a single solution,improving solution quality and eliminating inferior solutions.The WPES enables comprehensive exploration of the solution space,avoiding the problem of getting trapped in local optima.The algo-rithm is evaluated by comparing it with 10 other competitive algorithms on various PV models.The results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models,double diode models,and PV module models.It also exhibits robust performance under varying irradiation and temperature conditions.The study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models.展开更多
The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which ...The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.展开更多
This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,c...This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of self-strengthening.Furthermore,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO population.By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been verified.The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial.Compared to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent algorithms.To further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and temperatures.In summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.展开更多
Runge Kutta Optimization(RUN)is a widely utilized metaheuristic algorithm.However,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solv...Runge Kutta Optimization(RUN)is a widely utilized metaheuristic algorithm.However,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world opti-mization problems.To address these challenges,this study aims to endow each individual in the population with a certain level of intelligence,allowing them to make autonomous decisions about their next optimization behavior.By incorporating Reinforcement Learning(RL)and the Composite Mutation Strategy(CMS),each individual in the population goes through additional self-improvement steps after completing the original algorithmic phases,referred to as RLRUN.That is,each individual in the RUN population is trained intelligently using RL to independently choose three different differentiation strategies in CMS when solving different problems.To validate the competitiveness of RLRUN,comprehensive empirical tests were conducted using the IEEE CEC 2017 benchmark suite.Extensive comparative experiments with 13 conventional algorithms and 10 advanced algorithms were conducted.The experimental results demonstrated that RLRUN excels in convergence accuracy and speed,surpassing even some champion algorithms.Additionally,this study introduced a binary version of RLRUN,named bRLRUN,which was employed for the feature selection problem.Across 24 high-dimensional datasets encompassing UCI datasets and SBCB machine learning library microarray datasets,bRLRUN occupies the top position in classification accuracy and the number of selected feature subsets compared to some algorithms.In conclusion,the proposed algorithm demonstrated that it exhibits a strong competitive advantage in high-dimensional feature selection for complex datasets.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
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.
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.
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 Natural Science Foundation of Zhejiang Province(LY21F020001,LZ22F020005)National Natural Science Foundation of China(62076185)Science and Technology Plan Project of Wenzhou,China(ZG2020026).
文摘Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems.To accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,INMVO.Quantitative experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and exploitation.Further,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for comparison.In addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results statistically.Various evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other techniques.In addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and temperatures.In conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV models.Note that the source code of INMVO is available at https://github.com/woniuzuioupao/INMVO.
基金supported by MRC(MC_PC_17171)Royal Society(RP202G0230)+12 种基金BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(P202PF11)Sino-UK Industrial Fund(RP202G0289)LIAS(P202ED10,P202RE969)Data Science Enhancement Fund(P202RE237)Fight for Sight(24NN201)Sino-UK Education Fund(OP202006)BBSRC(RM32G0178B8)Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(62076185)The 18th batch of innovative and entrepreneurial talent funding projects in Jilin Province(No.49)Natural Science Foundation of Jilin Province(YDZJ202201ZYTS567).
文摘In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more complex.Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem.Among them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization performance.However,it tends to stagnate prematurely to local optima in solving some specific problems.Therefore,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called CGSE.In this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of SE.To objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with constraints.The final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real applications.Therefore,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LTGS23E070001)National Natural Science Foundation of China(62076185,62301367).
文摘This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency.RSWTLBO combines adaptive parameter w,Single Solution Optimization Mechanism(SSOM),and Weight Probability Exploration Strategy(WPES)to enhance the optimization ability of TLBO.The algorithm achieves a balance between exploitation and exploration throughout the iteration process.The SSOM allows for local exploration around a single solution,improving solution quality and eliminating inferior solutions.The WPES enables comprehensive exploration of the solution space,avoiding the problem of getting trapped in local optima.The algo-rithm is evaluated by comparing it with 10 other competitive algorithms on various PV models.The results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models,double diode models,and PV module models.It also exhibits robust performance under varying irradiation and temperature conditions.The study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(62076185,62301367).
文摘The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LTGS23E070001).
文摘This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed LNPRO.The LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of self-strengthening.Furthermore,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO population.By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been verified.The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial.Compared to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent algorithms.To further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and temperatures.In summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.
基金supported in part by Zhejiang Provincial Philosophy and Social Sciences Planning Project,China(23NDJC393YBM)the Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(62076185,62301367).
文摘Runge Kutta Optimization(RUN)is a widely utilized metaheuristic algorithm.However,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world opti-mization problems.To address these challenges,this study aims to endow each individual in the population with a certain level of intelligence,allowing them to make autonomous decisions about their next optimization behavior.By incorporating Reinforcement Learning(RL)and the Composite Mutation Strategy(CMS),each individual in the population goes through additional self-improvement steps after completing the original algorithmic phases,referred to as RLRUN.That is,each individual in the RUN population is trained intelligently using RL to independently choose three different differentiation strategies in CMS when solving different problems.To validate the competitiveness of RLRUN,comprehensive empirical tests were conducted using the IEEE CEC 2017 benchmark suite.Extensive comparative experiments with 13 conventional algorithms and 10 advanced algorithms were conducted.The experimental results demonstrated that RLRUN excels in convergence accuracy and speed,surpassing even some champion algorithms.Additionally,this study introduced a binary version of RLRUN,named bRLRUN,which was employed for the feature selection problem.Across 24 high-dimensional datasets encompassing UCI datasets and SBCB machine learning library microarray datasets,bRLRUN occupies the top position in classification accuracy and the number of selected feature subsets compared to some algorithms.In conclusion,the proposed algorithm demonstrated that it exhibits a strong competitive advantage in high-dimensional feature selection for complex datasets.
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.
