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在对石油射孔枪结构进行有限元静、动力分析的基础上,利用BP神经网络对有限元分析得出的样本数据,建立射孔枪结构设计参数盲孔处的最大应力(输入)与盲孔深度、盲孔直径(输出)的全局性映射关系,获得遗传算法求解结构优化问题所需的目标函数值.最后,用改进的遗传算法进行射孔枪结构的优化设计.结果表明,基于神经网络和遗传算法的优化技术应用在射孔枪结构的优化设计中是有效、合理的.从广义的角度,作为结构优化问题求解方法的一个探讨,本文所提出的优化技术,也为工程领域中复杂、多变量,尤其是优化设计目标无法或难以表示成设计变量的显函数的优化问题的求解提供了新的思路和技术手段. 相似文献
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简要介绍了改进遗传算法求解问题的步骤以及解决实际问题的特点。为了利用改进遗传算法的优点,提高其收敛速度,提出改进遗传算法与人工神经网络(BP网络)利用神经网络的联想记忆、特征提取功能辅助遗传算法求解结构优化设计问题,以避免在遗传算法中所作的那些不必要的分析计算,从而节省了计算时间。最后通过算例证实,比简单遗传算法与人工神经网络协作计算时间减少约25%。 相似文献
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基于遗传算法的神经网络性能优化 总被引:2,自引:0,他引:2
遗传算法是一种典型的进化算法。文中分析了遗传算法的特点和神经网络的特点,从而得出了把两种算法结合起来进行应用的思想。运用理论对比的方法,阐明了用遗传算法进行神经网络性能优化的原因,并得出结论,认为用遗传算法进行神经网络性能优化促使了神经网络更进一步的应用。阐述了遗传算法优化神经网络的两种主要方法,论述了遗传算法和神经网络的发展现状和将来的研究动向。 相似文献
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采用遗传算法求解桁架结构优化设计问题,建立了平面桁架结构优化的数学模型,应用改进的自适应遗传算法对其进行求解。为了加快遗传算法进化过程,本文采用精英选择与轮盘赌选择相结合的策略,鲁棒性更好,收敛速度更快,拥有较强的寻优能力。算例表明,该遗传算法可用于桁架结构的优化设计,优化速度快,效率高,优化结果更加可靠。 相似文献
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用遗传算法与自适应神经网络混合方法解Job-shop调度问题 总被引:2,自引:0,他引:2
提出一种用遗传算法结合基于约束满足的自适应神经网络进行Job—shop调度问题求解的混合方法。遗传算法被用来进行迭代寻优。当前代经交叉和变异后生成的染色体对应非可行解,由自适应神经网络运算后得到可行解,对应的染色体作为新一代染色体。仿真表明该算法是快速有效的 相似文献
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BP神经网络(Back Propagation Neural Network,BP-NN)具有良好的自学习能力以及自适应和泛化能力,但运算过程中容易陷入局部极小值,同时隐含层节点数的选择也影响着诊断的效果。文中根据经验公式缩小隐层节点数范围,在小范围里寻找最优的隐层节点数。根据遗传算法(Genetic Algorithm,GA)具有全局寻优的特点,用遗传算法优化BP神经网络训练的初始权值阈值,可以避免BP神经网络陷入局部极小的问题。但是,传统遗传算法也有自身的缺点,其在全局寻优的过程中,易陷入“早熟”的问题。为了解决传统遗传算法“早熟”现象,文中提出了一种协同进化的遗传算法,即使用3个种群同时进化的遗传算法,协同进化遗传算法不但可以避免传统遗传算法的“早熟”问题,而且可以加强局部搜索提高运行效率。将协同进化遗传算法应用到BP神经网络中,仿真结果表明,该方法可以准确有效地诊断出变电站故障元件,提高变电站故障诊断过程中的容错性及效果。 相似文献
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Dengiz B. Altiparmak F. Smith A.E. 《Evolutionary Computation, IEEE Transactions on》1997,1(3):179-188
This paper presents a genetic algorithm (GA) with specialized encoding, initialization, and local search operators to optimize the design of communication network topologies. This NP-hard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible networks. Another complication is that the fitness function involves calculating the all-terminal reliability of the network, which is a computationally expensive calculation. Therefore, it is imperative that the search balances the need to thoroughly explore the boundary between feasible and infeasible networks, along with calculating fitness on only the most promising candidate networks. The algorithm results are compared to optimum results found by branch and bound and also to GA results without local search operators on a suite of 79 test problems. This strategy of employing bounds, simple heuristic checks, and problem-specific repair and local search operators can be used on other highly constrained combinatorial applications where numerous fitness calculations are prohibitive 相似文献
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Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures. 相似文献
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In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research. 相似文献
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Omar Fayez Mohamed El-Mahdy Mohamed Ezz Hassan Ahmed Sayed Metwalli 《Applied Soft Computing》2010,10(4):1141-1150
This study is concerned to determine the optimum pipe size for networks used in natural gas applications. The genetic algorithm has been used in optimizing network parameters. The topology of the network is predefined. The study deals with the discrete nature of decision variables, namely, pipe diameters, as they are usually available in market in standard sizes. Hard constraints and soft constraints are considered. An imposed penalty factor is introduced to allow solutions that violate soft constraints to remain in the population during the solution progress guiding the algorithm convergence to a minimum network cost.In a case study, engineers with average experience of 6 years in the design office of a gas company performed the design of a gas network problem using their experience and judgment. The adopted method by engineers depends on a trial and error, time consuming, procedure. Their results are compared with the results obtained from the developed genetic algorithm optimization technique.The developed optimization technique has provided a distinctive reduction in the total cost of pipe networks over the existing heuristic approach which is based on human experience and judgment. A saving up to 12.1% has been achieved using the present analysis, in the special case studied. 相似文献
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遗传算法在反应器网络综合中的应用 总被引:2,自引:2,他引:0
反应器网络综合问题一般都是复杂的非线性规划问题,遗传算法作为一种启发式全局优化方法,具有计算简单、功能性强的特点。本文将全局优化遗传算法应用到反应器网络综合中以避免传统的优化方法很难求得其全局最优解的缺点。本文首先分析了几种反应器的替代结构,以及在此基础上建立的通用的全混流反应器(Continuous Stirred Tank Reactor,CSTR)网络结构模型;然后采用全局优化遗传算法以及传统的数学优化工具GAMS(General Algorithm Model System)分别对该模型进行求解。实例研究表明,遗传算法可有效地求解此类反应器网络综合问题,且其计算结果优于传统优化方法的结果。 相似文献
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《Artificial Intelligence in Engineering》1999,13(3):251-256
Design synthesis represents a highly complex task in the field of industrial design. The main difficulty in automating it is the definition of the design and performance spaces, in a way that a computer can generate optimum solutions. Following a different line from the machine learning, and knowledge-based methods that have been proposed, our approach considers design synthesis as an optimization problem. From this outlook, neural networks and genetic algorithms can be used to implement the fitness function and the search method needed to achieve optimum design. The proposed method has been tested in designing a telephone handset. Although the objective of this application is based on esthetic and ergonomic cues (subjective information), the algorithm successfully converges to good solutions. 相似文献
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This paper presents the topological design of ad hoc networks in terms of distances among static nodes and speeds of mobiles nodes. Due to the complexity of the problem and the number of parameters to be considered, a genetic algorithm combined with the simulation environment NS-2 is proposed to find the optimum solution. More specifically, NS-2 provides the fitness function guiding the genetic search. The proposed framework has been tested using a railway scenario in which several static and mobile nodes are interacting. Results show the feasibility of the proposed framework and illustrate the possibility of genetic approach for solving similar application scenarios. 相似文献
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《Advances in Engineering Software》2007,38(7):475-487
The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for cost optimization of bridge deck configurations. In the present work, ANN is used to predict the structural design responses which are used further in evaluation of fitness and constraint violation in GA process. A multilayer back-propagation neural network is trained with the results obtained using grillage analysis program for different bridge deck configurations and the correlation between sectional parameters and design responses has been established. Subsequently, GA is employed for arriving at optimum configuration of the bridge deck system by minimizing the total cost. By integrating ANN with GA, the computational time required for obtaining optimal solution could be reduced substantially. The efficacy of this approach is demonstrated by carrying out studies on cost optimization of T-girder bridge deck system for different spans. The method presented in this paper, would greatly reduce the computational effort required to find the optimum solution and guarantees bridge engineers to arrive at the near-optimal solution that could not be easily obtained using general modeling programs or by trial-and-error. 相似文献
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Vibration reduction optimum design of a steam-turbine rotor-bearing system using a hybrid genetic algorithm 总被引:1,自引:0,他引:1
This paper describes the vibration optimum design for the low-pressure steam-turbine rotor of a 1007-MW nuclear power plant by using a hybrid genetic algorithm (HGA) that combines a genetic algorithm and a local concentration search algorithm using a modified simplex method. This algorithm not only calculates the optimum solution faster and more accurately than the standard genetic algorithm but can also find the global and local optimum solutions. The objective function is to minimize the resonance response (Q-factor) of the second occurring mode in the excessive vibration. Under the constraints of shaft diameter, bearing length and clearance, these factors play a very important role in the design of a rotor-bearing system. In the present work, the shaft diameter, bearing length and clearance are chosen as the design variables. The results show that the HGA can reduce the excessive response at the critical speed and improve the stability. 相似文献