The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear o...The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.展开更多
Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have...Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation;however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.展开更多
There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced se...There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.展开更多
In this paper, we first reformulate the max-min dispersion problem as a saddle-point problem. Specifically, we introduce an auxiliary problem whose optimum value gives an upper bound on that of the original problem. T...In this paper, we first reformulate the max-min dispersion problem as a saddle-point problem. Specifically, we introduce an auxiliary problem whose optimum value gives an upper bound on that of the original problem. Then we propose the saddle-point problem to be solved by an adaptive custom proximal point algorithm. Numerical results show that the proposed algorithm is efficient.展开更多
The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such ...The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.展开更多
The bonding of steel and mushy Al 28Pb alloy was studied. The relationship model about preheat temperature of steel plate, solid fraction of mushy Al 28Pb alloy, rolling speed and interfacial shear strength of the bon...The bonding of steel and mushy Al 28Pb alloy was studied. The relationship model about preheat temperature of steel plate, solid fraction of mushy Al 28Pb alloy, rolling speed and interfacial shear strength of the bonding plate was established by artificial neural network perfectly. This model can be optimized by a genetic algorithm, and the optimum bonding parameters for the largest interfacial shear strength are: 546 ℃ for preheat temperature of steel plate, 43.5% for solid fraction of mushy Al 28Pb alloy and 8.6 mm/s for rolling speed, and the corresponding largest interfacial shear strength of bonding plate is 70.3 MPa. [展开更多
The bonding of steel plate to Al 7graphite slurry was studied for the first time. The relationship model about preheat temperature of steel plate,solid fraction of Al 7graphite slurry, rolling speed and interfacial ...The bonding of steel plate to Al 7graphite slurry was studied for the first time. The relationship model about preheat temperature of steel plate,solid fraction of Al 7graphite slurry, rolling speed and interfacial shear strength of bonding plate could be established by artificial neural networks perfectly.This model could be optimized with a genetic algorithm.The optimum bonding parameters are:516℃ for preheat temperature of steel plate,32.5% for solid fraction of Al 7graphite slurry and 12mm/s for rolling speed,and the largest interfacial shear strength of bonding plate is 70.6MPa.展开更多
基金supported bythe National Natural Science Foundation of China(Grant Nos40975063 and 40830955)
文摘The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.
文摘Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation;however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.
基金supported by the Aviation Science Funds of China(2010ZC13012)the Fund of Jiangsu Innovation Program for Graduate Education (CXLX11 0203)
文摘There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.
文摘In this paper, we first reformulate the max-min dispersion problem as a saddle-point problem. Specifically, we introduce an auxiliary problem whose optimum value gives an upper bound on that of the original problem. Then we propose the saddle-point problem to be solved by an adaptive custom proximal point algorithm. Numerical results show that the proposed algorithm is efficient.
文摘The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.
文摘The bonding of steel and mushy Al 28Pb alloy was studied. The relationship model about preheat temperature of steel plate, solid fraction of mushy Al 28Pb alloy, rolling speed and interfacial shear strength of the bonding plate was established by artificial neural network perfectly. This model can be optimized by a genetic algorithm, and the optimum bonding parameters for the largest interfacial shear strength are: 546 ℃ for preheat temperature of steel plate, 43.5% for solid fraction of mushy Al 28Pb alloy and 8.6 mm/s for rolling speed, and the corresponding largest interfacial shear strength of bonding plate is 70.3 MPa. [
文摘The bonding of steel plate to Al 7graphite slurry was studied for the first time. The relationship model about preheat temperature of steel plate,solid fraction of Al 7graphite slurry, rolling speed and interfacial shear strength of bonding plate could be established by artificial neural networks perfectly.This model could be optimized with a genetic algorithm.The optimum bonding parameters are:516℃ for preheat temperature of steel plate,32.5% for solid fraction of Al 7graphite slurry and 12mm/s for rolling speed,and the largest interfacial shear strength of bonding plate is 70.6MPa.
