A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and le...A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t...In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.展开更多
To improve the cooling performance, shape optimization of a laidback fan-shaped film cooling hole was performed. Three geometric parameters, including hole length, lateral expansion angle and forward expansion angle, ...To improve the cooling performance, shape optimization of a laidback fan-shaped film cooling hole was performed. Three geometric parameters, including hole length, lateral expansion angle and forward expansion angle, were selected as the design parameters. Numerical model of the film cooling system was established, validated, and used to generate 32 groups of training samples. Least square support vector machine(LS-SVM) was applied for surrogate model, and the optimal design parameters were determined by a kind of chaotic optimization algorithm. As hole length, lateral expansion angle and forward expansion angle are 90 mm, 20° and 5°, the area-averaged film cooling effectiveness can reach its maximum value in the design space. LS-SVM coupled with chaotic optimization algorithm is a promising scheme for the optimization of shaped film cooling holes.展开更多
In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of sh...In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.展开更多
Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of classification, SVM...Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of classification, SVM kernel parameter setting during the SVM training procedure, along with the feature selection significantly influences the classification accuracy. This paper proposes two novel intelligent optimization methods, which simultaneously determines the parameter values while discovering a subset of features to increase SVM classification accuracy. The study focuses on two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine above the two intelligent optimization methods with SVM to choose appropriate subset features and SVM parameters, which are termed GA-FSSVM (Genetic Algorithm-Feature Selection Support Vector Machines) and PSO-FSSVM(Particle Swarm Optimization-Feature Selection Support Vector Machines) models. Experimental results demonstrate that the classification accuracy by our proposed methods outperforms traditional grid search approach and many other approaches. Moreover, the result indicates that PSO-FSSVM can obtain higher classification accuracy than GA-FSSVM classification for hyperspectral data.展开更多
Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics,integral for early detection and effective treatment.While deep learning has significantly advanced the analy...Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics,integral for early detection and effective treatment.While deep learning has significantly advanced the analysis of mammographic images,challenges such as low contrast,image noise,and the high dimensionality of features often degrade model performance.Addressing these challenges,our study introduces a novel method integrating Genetic Algorithms(GA)with pre-trained Convolutional Neural Network(CNN)models to enhance feature selection and classification accuracy.Our approach involves a systematic process:first,we employ widely-used CNN architectures(VGG16,VGG19,MobileNet,and DenseNet)to extract a broad range of features from the Medical Image Analysis Society(MIAS)mammography dataset.Subsequently,a GA optimizes these features by selecting the most relevant and least redundant,aiming to overcome the typical pitfalls of high dimensionality.The selected features are then utilized to train several classifiers,including Linear and Polynomial Support Vector Machines(SVMs),K-Nearest Neighbors,Decision Trees,and Random Forests,enabling a robust evaluation of the method’s effectiveness across varied learning algorithms.Our extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy,from 83.33%to 89.58%,underscoring the method’s efficacy.By detailing these steps,we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging.展开更多
Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques....Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.展开更多
文摘A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.
基金supported by the Center for Mining,Electro-Mechanical Research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnam。
文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.
基金Project(U1508212)supported by the National Natural Science Foundation of ChinaProject(2015M570448)supported by the Postdoctoral Science Foundation of China
文摘To improve the cooling performance, shape optimization of a laidback fan-shaped film cooling hole was performed. Three geometric parameters, including hole length, lateral expansion angle and forward expansion angle, were selected as the design parameters. Numerical model of the film cooling system was established, validated, and used to generate 32 groups of training samples. Least square support vector machine(LS-SVM) was applied for surrogate model, and the optimal design parameters were determined by a kind of chaotic optimization algorithm. As hole length, lateral expansion angle and forward expansion angle are 90 mm, 20° and 5°, the area-averaged film cooling effectiveness can reach its maximum value in the design space. LS-SVM coupled with chaotic optimization algorithm is a promising scheme for the optimization of shaped film cooling holes.
基金Supported by the Project of Ministry of Education and Finance (No.200512)the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10)
文摘In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.
文摘Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of classification, SVM kernel parameter setting during the SVM training procedure, along with the feature selection significantly influences the classification accuracy. This paper proposes two novel intelligent optimization methods, which simultaneously determines the parameter values while discovering a subset of features to increase SVM classification accuracy. The study focuses on two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine above the two intelligent optimization methods with SVM to choose appropriate subset features and SVM parameters, which are termed GA-FSSVM (Genetic Algorithm-Feature Selection Support Vector Machines) and PSO-FSSVM(Particle Swarm Optimization-Feature Selection Support Vector Machines) models. Experimental results demonstrate that the classification accuracy by our proposed methods outperforms traditional grid search approach and many other approaches. Moreover, the result indicates that PSO-FSSVM can obtain higher classification accuracy than GA-FSSVM classification for hyperspectral data.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,“Ministry of Education”in Saudi Arabia for funding this research work through the project number (IFKSUDR_D127).
文摘Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics,integral for early detection and effective treatment.While deep learning has significantly advanced the analysis of mammographic images,challenges such as low contrast,image noise,and the high dimensionality of features often degrade model performance.Addressing these challenges,our study introduces a novel method integrating Genetic Algorithms(GA)with pre-trained Convolutional Neural Network(CNN)models to enhance feature selection and classification accuracy.Our approach involves a systematic process:first,we employ widely-used CNN architectures(VGG16,VGG19,MobileNet,and DenseNet)to extract a broad range of features from the Medical Image Analysis Society(MIAS)mammography dataset.Subsequently,a GA optimizes these features by selecting the most relevant and least redundant,aiming to overcome the typical pitfalls of high dimensionality.The selected features are then utilized to train several classifiers,including Linear and Polynomial Support Vector Machines(SVMs),K-Nearest Neighbors,Decision Trees,and Random Forests,enabling a robust evaluation of the method’s effectiveness across varied learning algorithms.Our extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy,from 83.33%to 89.58%,underscoring the method’s efficacy.By detailing these steps,we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging.
基金This work was supported by the Research Grant of SEC E-Institute :Shanghai High Institution Grid and the Science Foundation ofShanghai Municipal Commission of Science and Technology No.00JC14052
文摘Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.
