An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using c...An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.展开更多
Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback predicti...Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback prediction in stretch bending forming mainly focuses on artificial neural networks combined with the finite element simulation. There is a lack of springback prediction by support vector regression（SVR）. In this paper, SVR is applied to predict springback in the three-dimensional stretch bending forming process, and variable stretch force trajectory is optimized. Six parameters of variable stretch force trajectory are chosen as the input parameters of the SVR model. Sixty experiments generated by design of experiments（DOE） are carried out to train and test the SVR model. The experimental results confirm that the accuracy of the SVR model is higher than that of artificial neural networks. Based on this model, an optimization algorithm of variable stretch force trajectory using particle swarm optimization（PSO） is proposed. The springback amount is used as the objective function. Changes of local thickness are applied as the criterion of forming constraints. The objection and constraints are formulated by response surface models. The precision of response surface models is examined. Six different stretch force trajectories are employed to certify springback reduction in the optimum stretch force trajectory, which can efficiently reduce springback. This research proposes a new method of springback prediction using SVR and optimizes variable stretch force trajectory to reduce springback.展开更多
This paper focuses on a method to solve structural optimization problems using particle swarm optimization （PSO）, surrogate models and Bayesian statistics. PSO is a random/stochastic search algorithm designed to fin...This paper focuses on a method to solve structural optimization problems using particle swarm optimization （PSO）, surrogate models and Bayesian statistics. PSO is a random/stochastic search algorithm designed to find the global optimum. However, PSO needs many evaluations compared to gradient-based optimization. This means PSO increases the analysis costs of structural optimization. One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques. In this work, surrogate models are used, including the response surface method （RSM） and Kriging. When surrogate models are used, there are some errors between exact values and approximated values. These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models. In this paper, Bayesian statistics is used to obtain more reliable results. To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization, two numerical examples are optimized, and the optimization of a hub sleeve is demonstrated as a practical problem.展开更多
This paper presents the implementation and application of a modified particle swarm optimization(PSO) method with dynamic adaption for optimum design of a battleship strength deck subjected to non-contact explosion. T...This paper presents the implementation and application of a modified particle swarm optimization(PSO) method with dynamic adaption for optimum design of a battleship strength deck subjected to non-contact explosion. The numerical simulation process is modified to be more computationally efficient so that the task is realizable. The input variables are the thickness of plates and the dimensions of stiffeners, and the total structural mass is chosen as the fitness value. In another case, the response surface method(RSM) is introduced and combined with PSO(PSO-RSM), and the results are compared with those obtained by the traditional PSO approach. It is indicated that the PSO method can be well applied in the optimum design of explosion-loaded deck structures and the PSO-RSM methodology can rapidly yield optimum designs with sufficient accuracy.展开更多
A system combining photovoltaic power generation and cogeneration is proposed to improve the photoelectric absorption capacity. First, a time-of-use price strategy is adopted to guide users to change their electricity...A system combining photovoltaic power generation and cogeneration is proposed to improve the photoelectric absorption capacity. First, a time-of-use price strategy is adopted to guide users to change their electricity consumption habits for participation in the demand response, and a demand response model is established. Then, particle swarm optimization(PSO)is used with the aim of minimizing the operation cost of the microgrid to achieve economic dispatching of the microgrid. This considers power balance equation constraints, unit operation constraints, energy storage constraints, and heat storage constraints. Finally, the simulation results show the improved level of photoelectric consumption using the proposed scheme and the economic benefits of the microgrid.展开更多
提出了一种适用于电机优化设计的参数化网格剖分方法。该方法将所有剖分节点坐标表示为一组几何参数的线性函数,当电机的几何尺寸变化时不需要网格重新剖分,能显著减少有限元(finite element method,FEM)预处理时间,并通过自适应网格剖...提出了一种适用于电机优化设计的参数化网格剖分方法。该方法将所有剖分节点坐标表示为一组几何参数的线性函数,当电机的几何尺寸变化时不需要网格重新剖分,能显著减少有限元(finite element method,FEM)预处理时间,并通过自适应网格剖分方法和交换对角线技术提高网格单元质量。对电机的参数化网格剖分模型进行FEM计算,利用表面响应法对FEM计算的结果进行处理,构建电机的优化模型,再利用粒子群优化算法寻求优化模型的最优解,得到电机的最优结构参数。将该方法应用于电机优化设计的两个实例,验证了其有效性。展开更多
文摘An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.
基金Supported by National Technical Innovation Foundation of China(Grant No.Jilin Province 350)
文摘Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback prediction in stretch bending forming mainly focuses on artificial neural networks combined with the finite element simulation. There is a lack of springback prediction by support vector regression（SVR）. In this paper, SVR is applied to predict springback in the three-dimensional stretch bending forming process, and variable stretch force trajectory is optimized. Six parameters of variable stretch force trajectory are chosen as the input parameters of the SVR model. Sixty experiments generated by design of experiments（DOE） are carried out to train and test the SVR model. The experimental results confirm that the accuracy of the SVR model is higher than that of artificial neural networks. Based on this model, an optimization algorithm of variable stretch force trajectory using particle swarm optimization（PSO） is proposed. The springback amount is used as the objective function. Changes of local thickness are applied as the criterion of forming constraints. The objection and constraints are formulated by response surface models. The precision of response surface models is examined. Six different stretch force trajectories are employed to certify springback reduction in the optimum stretch force trajectory, which can efficiently reduce springback. This research proposes a new method of springback prediction using SVR and optimizes variable stretch force trajectory to reduce springback.
文摘This paper focuses on a method to solve structural optimization problems using particle swarm optimization （PSO）, surrogate models and Bayesian statistics. PSO is a random/stochastic search algorithm designed to find the global optimum. However, PSO needs many evaluations compared to gradient-based optimization. This means PSO increases the analysis costs of structural optimization. One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques. In this work, surrogate models are used, including the response surface method （RSM） and Kriging. When surrogate models are used, there are some errors between exact values and approximated values. These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models. In this paper, Bayesian statistics is used to obtain more reliable results. To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization, two numerical examples are optimized, and the optimization of a hub sleeve is demonstrated as a practical problem.
文摘This paper presents the implementation and application of a modified particle swarm optimization(PSO) method with dynamic adaption for optimum design of a battleship strength deck subjected to non-contact explosion. The numerical simulation process is modified to be more computationally efficient so that the task is realizable. The input variables are the thickness of plates and the dimensions of stiffeners, and the total structural mass is chosen as the fitness value. In another case, the response surface method(RSM) is introduced and combined with PSO(PSO-RSM), and the results are compared with those obtained by the traditional PSO approach. It is indicated that the PSO method can be well applied in the optimum design of explosion-loaded deck structures and the PSO-RSM methodology can rapidly yield optimum designs with sufficient accuracy.
基金supported by the key projects of the National Natural Science Foundation of China (No.61833008,No.61573300)Jiangsu Provincial Natural Science Foundation of China (No.BK20171445)Key Research and Development Plan of Jiangsu Province (No.BE2016184)。
文摘A system combining photovoltaic power generation and cogeneration is proposed to improve the photoelectric absorption capacity. First, a time-of-use price strategy is adopted to guide users to change their electricity consumption habits for participation in the demand response, and a demand response model is established. Then, particle swarm optimization(PSO)is used with the aim of minimizing the operation cost of the microgrid to achieve economic dispatching of the microgrid. This considers power balance equation constraints, unit operation constraints, energy storage constraints, and heat storage constraints. Finally, the simulation results show the improved level of photoelectric consumption using the proposed scheme and the economic benefits of the microgrid.
文摘提出了一种适用于电机优化设计的参数化网格剖分方法。该方法将所有剖分节点坐标表示为一组几何参数的线性函数,当电机的几何尺寸变化时不需要网格重新剖分,能显著减少有限元(finite element method,FEM)预处理时间,并通过自适应网格剖分方法和交换对角线技术提高网格单元质量。对电机的参数化网格剖分模型进行FEM计算,利用表面响应法对FEM计算的结果进行处理,构建电机的优化模型,再利用粒子群优化算法寻求优化模型的最优解,得到电机的最优结构参数。将该方法应用于电机优化设计的两个实例,验证了其有效性。
