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1.
BP神经网络是目前水文预报中应用较为广泛的方法,但存在收敛速度慢、易陷入局部最优的缺陷.由此提出了基于全局优化打洞函数法的水文预报方法,把打洞函数法和BP神经网络相结合,利用打洞函数使BP算法跳出当前局部极小点,得到一个函数值更小的极小点,循环运算直至找到全局极小点.实验表明该水文预报方法能够提高预报精度,显示了良好的...  相似文献   

2.
全局优化是最优化的一个分支,非线性整数规划问题的全局优化在各个方面都有广泛的应用.填充函数是解决全局优化问题的方法之一,它可以帮助目标函数跳出当前的局部极小点找到下一个更好的极小点.滤子方法的引入可以使得目标函数和填充函数共同下降,省却了以往算法要设置两个循环的麻烦,提高了算法的效率.本文提出了一个求解无约束非线性整数规划问题的无参数填充函数,并分析了其性质.同时引进了滤子方法,在此基础上设计了整数规划的无参数滤子填充函数算法.数值实验证明该算法是有效的.  相似文献   

3.
由任意初始点求解离散型约束全局优化问题   总被引:1,自引:1,他引:0  
徐语论  赵德芬  王薇 《数学杂志》2011,31(3):539-546
本文研究了带约束离散型非线性全局优化的求解问题.利用0-1变量提出了一个离散填充函数算法.该算法可由任意初始点出发,不断求得更好的局部极小点,以期得到离散全局最小点.文章同时讨论了所构造的填充函数的性质,给出了数值试验结果.  相似文献   

4.
本文综述了七十年代以来全局优化问题随机型方法的若干研究成果,重点是最近几年的某些新结果.§1 引言全局优化问题足寻求实值日标函数 f:R~n→R 的全局极值点(例如极小点)x,即求一点 x∈R~n 使得  相似文献   

5.
实现快速全局优化的跨越函数方法   总被引:1,自引:0,他引:1  
本文提出了一种快速求解全局优化问题的跨越函数方法,与以填充函数法为代表的一类全局优化方法相比,本文定义的跨越函数直接凸显了在求解全局优化问题时构造辅助函数的目的,更重要的是跨越函数方法能够一步跨过函数值比当前局部极小值高的区域,而直接找到原函数f(x)的位于函数值比当前局部极小值低的区域中的局部极小点,加快了全局寻优的过程,并且通过有限次迭代,找到全局最优解.  相似文献   

6.
反演分析是现场监测⁃反演分析⁃工程实践检验⁃正演分析及预测的闭环系统的重要环节,而参数反分析是工程实践中研究最多的反分析问题.针对混凝土重力坝多参数反演分析是否具有唯一性,基于均质地基上重力坝在水压力作用下的位移解析解建立目标函数,进而以目标函数和非空凸集构建一个凸规划问题,然后通过分析目标函数的Hesse矩阵是否是正定矩阵,验证目标函数是否是严格凸函数,从而辨识构建的凸规划问题是否具有唯一全局极小点.对坝体和岩基弹性参数的不同组合方案分析表明,当采用理论值与实测值的差值的l1范数作为目标函数时,目标函数的Hesse矩阵均不能保证为正定矩阵,即混凝土重力坝多参数弹性位移反演分析凸规划问题不具有唯一全局极小点,反演分析不唯一.  相似文献   

7.
提出一种基于Hamilton路模型的新方法研究蛋白质结构预测问题,为使结构匹配序列,把已知蛋白质的3D结构信息转化为一个加权的完全图Kn,则求这个特定空间结构所匹配的氨基酸残基序列问题转化为求Kn图的最小H路问题.用此方法研究了72个单链蛋白质结构,结果表明Kn图的最小H路对应此蛋白质的序列,图的顶点数n与最小H路总长度成正比.  相似文献   

8.
根据电力负荷预测的特点,提出遗传神经网络负荷预测模型,有效地克服了人工神经网络学习速度慢、存在局部极小点的固有缺陷,经实例验证,该方法能有效地提高预测精度和速度。  相似文献   

9.
基于最优化方法求解约束非线性方程组的一个突出困难是计算 得到的仅是该优化问题的稳定点或局部极小点,而非方程组的解点.由此引出的问题是如何从一个稳定点出发得到一个相对于方程组解更好的点. 该文采用投影型算法,推广了Nazareth-Qi$^{[8,9]}$ 求解无约束非线性方程组的拉格朗日全局算法(Lagrangian Global-LG)于约束方程上; 理论上证明了从优化问题的稳定点出发,投影LG方法可寻找到一个更好的点. 数值试验证明了LG方法的有效性.  相似文献   

10.
求解Lipschitz型规划全局极小点的改进的填充函数法   总被引:4,自引:0,他引:4  
1 引言 考虑问题 (P)min(x), x∈Ω其中F:ΩR~n→R是局部Lipschitz函数,Ω为紧集,且F(x)在Ω内有极小点。文[1,2,3]在一定条件下给出了求解一般非光滑规划全局极小点的填充函数法,并给出了求解的全过程。本文根据文[1,2,3]的思想,为求解(P),结合函数的特点,给出了一种改进  相似文献   

11.
A deterministic global optimization method is described for identifying the global minimum potential energy conformation of oligopeptides. The ECEPP/3 detailed potential energy model is utilized for describing the energetics of the atomic interactions posed in the space of the peptide dihedral angles. Based on previous work on the microcluster and molecular structure determination [21, 22, 23, 24], a procedure for deriving convex lower bounding functions for the total potential energy function is developed. A procedure that allows the exclusion of domains of the (ø, ) space based on the analysis of experimentally determined native protein structures is presented. The reduced disjoint sub-domains are appropriately combined thus defining the starting regions for the search. The proposed approach provides valuable information on (i) the global minimum potential energy conformation, (ii) upper and lower bounds of the global minimum energy structure and (iii) low energy conformers close to the global minimum one. The proposed approach is illustrated with Ac-Ala4-Pro-NHMe, Met-enkephalin, Leu-enkephalin, and Decaglycine.  相似文献   

12.
First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the αBB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations.  相似文献   

13.
In this paper we propose an algorithm using only the values of the objective function and constraints for solving one-dimensional global optimization problems where both the objective function and constraints are Lipschitzean and nonlinear. The constrained problem is reduced to an unconstrained one by the index scheme. To solve the reduced problem a new method with local tuning on the behavior of the objective function and constraints over different sectors of the search region is proposed. Sufficient conditions of global convergence are established. We also present results of some numerical experiments.  相似文献   

14.
We develop a continuous variable neighborhood search heuristic for minimizing the potential energy function of a molecule. Computing the global minimum of this function is very difficult because it has a large number of local minimizers which grows exponentially with molecule size. Experimental evidence shows that in the great majority of cases the global minimum potential energy of a given molecule corresponds to its three-dimensional structure and this structure is important because it dictates most of the properties of the molecule. Computational results for problems with up to 200 degrees of freedom are presented and favourable compared with other two existing methods from the literature.  相似文献   

15.
一种改进的禁忌搜索算法及其在连续全局优化中的应用   总被引:2,自引:1,他引:1  
禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。  相似文献   

16.
A new subspace minimization conjugate gradient algorithm with a nonmonotone Wolfe line search is proposed and analyzed. In the scheme, we propose two choices of the search direction by minimizing a quadratic approximation of the objective function in special subspaces, and state criterions on how to choose the direction. Under given conditions, we obtain the significant conclusion that each choice of the direction satisfies the sufficient descent property. Based on the idea on how the function is close to a quadratic function, a new strategy for choosing the initial stepsize is presented for the line search. With the used nonmonotone Wolfe line search, we prove the global convergence of the proposed method for general nonlinear functions under mild assumptions. Numerical comparisons are given with well-known CGOPT and CG_DESCENT and show that the proposed algorithm is very promising.  相似文献   

17.
We describe a large-scale, stochastic-perturbation global optimization algorithm used for determining the structure of proteins. The method incorporates secondary structure predictions (which describe the more basic elements of the protein structure) into the starting structures, and thereafter minimizes using a purely physics-based energy model. Results show this method to be particularly successful on protein targets where structural information from similar proteins is unavailable, i.e., the most difficult targets for most protein structure prediction methods. Our best result to date is on a protein target containing over 4000 atoms and 12,000 cartesian coordinates.  相似文献   

18.
This paper proposes and analyzes an affine scaling trust-region method with line search filter technique for solving nonlinear optimization problems subject to bounds on variables. At the current iteration, the trial step is generated by the general trust-region subproblem which is defined by minimizing a quadratic function subject only to an affine scaling ellipsoidal constraint. Both trust-region strategy and line search filter technique will switch to trail backtracking step which is strictly feasible. Meanwhile, the proposed method does not depend on any external restoration procedure used in line search filter technique. A new backtracking relevance condition is given which is weaker than the switching condition to obtain the global convergence of the algorithm. The global convergence and fast local convergence rate of this algorithm are established under reasonable assumptions. Preliminary numerical results are reported indicating the practical viability and show the effectiveness of the proposed algorithm.  相似文献   

19.
提出了一种解非线性规划问题的修改的非单调线搜索算法,并给出了它的全局收敛性证明.不需要用罚函数作为价值函数,也不用滤子和可行性恢复阶段.该算法是基于多目标优化的思想一个迭代点被接受当且仅当目标函数值或是约束违反度函数值有充分的下降.数值结果与LANCELOT作了比较,表明该算法是可靠的.  相似文献   

20.
In this paper we present a method called NOVEL (Nonlinear Optimization via External Lead) forsolving continuous and discrete global optimization problems. NOVEL addresses the balance between global search and local search, using a trace to aid in identifying promising regions before committing to local searches. We discuss NOVEL for solving continuous constrained optimization problems and show how it can be extended to solve constrained satisfaction and discrete satisfiability problems. We first transform the problem using Lagrange multipliers into an unconstrained version. Since a stable solution in a Lagrangian formulation only guarantees a local optimum satisfying the constraints, we propose a global search phase in which an aperiodic and bounded trace function is added to the search to first identify promising regions for local search. The trace generates an information-bearing trajectory from which good starting points are identified for further local searches. Taking only a small portion of the total search time, this elegant approach significantly reduces unnecessary local searches in regions leading to the same local optimum. We demonstrate the effectiveness of NOVEL on a collection of continuous optimization benchmark problems, finding the same or better solutions while satisfying the constraints. We extend NOVEL to discrete constraint satisfaction problems (CPSs) by showing an efficient transformation method for CSPs and the associated representation in finite-difference equations in NOVEL. We apply NOVEL to solve Boolean satisfiability instances in circuit fault detection and circuit synthesis applications, and show comparable performance when compared to the best existing method.  相似文献   

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