最后应用枚举法，得到全局最优解。
Lastly the global optimized solution can be got by enumerating.
算例表明该模型对求解全局最优解是有效的。
The example shows the efficiency and the global ability of the extended Gauss model.
求解非线性约束规划的全局最优解是一个难点。
Solving the global optimal solution for nonlinear constrained programming is difficult.
最后利用遗传算法获得威胁网络下的全局最优解。
The global optimal flight path could be obtained by using genetic algorithms.
业已证明，提出的算法可以保证收敛到全局最优解。
It has been proved that the algorithm can converge to globally optimal solution.
因此需要引进一些非线性优化算法求解全局最优解。
Some nonlinear algorithms are needed to achieve global optimal solutions.
算例结果表明，用该方法能以较快的速度收敛于全局最优解。
The example result shows that in the genetic algorithm the optimum global solution can be gained and the convergence speed is fast.
多峰值函数优化结果表明，该算法能更有效地达到全局最优解。
The optimization result of multi-peak value function shows that the algorithm introduced can converge to the globally optimal solution effectively.
通过对大量数据进行统计，用交互迭代的方法求出全局最优解。
Global optimum solution may be obtained by making interactive iteration and statistical analysis of a great deal of data.
目前对有约束非线性规划问题还没有通用的求全局最优解的算法。
Currently, there is no general algorithm to find the global optimal solution for the constrained non-linear programming problems.
进化规划(EP)是一种随机优化技术，它可以发现全局最优解。
Evolutionary Programming (EP), a multi agent stochastic optimization technique, can lead to global optimal solutions for complex problems.
导出了选择和变异条件下平均适应度单调递增并收敛到全局最优解的条件。
Sufficient condition for monotonous increase of the average fitness converging to the global maximum value is derived.
利用粒子群算法和差分进化算法的优点，可以获得测向问题的全局最优解。
The ADPSO is a global optimization algorithm for direction finding, which takes advantage of the merits of differential evolutionary algorithm and particle swarm algorithm.
实验结果表明，该算法能够保证求得全局最优解，并且寻优速度有很大提高。
The experiment shows the algorithm can surely and rapidly get global optimum solution and greatly improve con vergence rate.
算法的学习结果与网络的初始状态无关，并且十分接近于全局最优解的下限。
The study results are irrelevant to the initial status of the network and quite approximate to the lower limit of the optimum solutions.
由于进化计算一个最重要的特点便是全局搜索，这样可得全局最优解或次优解。
While one outstanding character of evolutionary computation is global search, it can converge to a globally optimal solution or at least a sub optimal solution.
针对一类非线性比式和问题首次提出一种求其全局最优解的单纯形分枝定界算法。
This paper presents for the first time a simplicial branch and bound algorithm for globally solving a class of nonlinear sum of ratios problem.
计算结果表明，该算法能快速地求出问题的全局最优解，且具有较好的计算精度。
The computing results show that it not only can find rapidly the approximate global optimal solution, but also has high computing accuracy.
理论上已经证明要在多项式时间复杂度内对这一类问题找到全局最优解是不可能的。
The fact that it is impossible to find the global optimum in polynomial complexity has been proved.
采用线性矩阵不等式技术，将问题转化为一线性凸优化算法，可得问题的全局最优解。
Using the linear matrix inequality (LMI) technique, the problem is converted into a linear convex optimization algorithm so that a global optimization solution is obtained. Finally.
为更好地求解复杂优化问题的全局最优解，提出了基于串联协作的多方法协作优化方法。
Multimethod collaborative optimization algorithm based on serial collaboration is advanced to get better global optima of complex optimization problem.
多峰值函数优化结果表明，该算法可以有效地解决早熟收敛问题，更易达到全局最优解。
The optimization result of multi-peak value function shows that the algorithm presented can solve premature convergence problem effectively and converge to the globally optimal solution.
针对算法的缺陷，对信息素更新策略进行了优化改进，使其能更快的收敛到全局最优解。
In allusion to the bugs of the model, the update strategies of information track were improved, so that the algorithm could converge to global optimal solution quickly.
本文针对一类带有反凸约束的凸函数比式和问题提出了一种求其全局最优解的分支定界算法。
This article presents a branch and bound algorithm for globally solving the sum of convex-convex ratios problem with nonconvex feasible region.
遗传过程中，通过保持迭代过程中的最优解，加快了搜索速度，并保证了收敛于全局最优解。
And, the searching speed is improved by holding the optimum in iteration, which can guarantee the global optimal solution being found as well.
将基于直接搜索法的随机全局优化方法用于求解该问题的全局最优解，给出了具体的算法步骤。
A stochastic global optimization method based on direct search is introduced to solve the global optimal solution of the problem, and the process is also discussed in detail.
假定约束是可行、规范的，对于目标函数为正定或半正定的情形，得到了全局最优解的充要条件。
Under the cases that the objective function is positive definite or positive semidefinite, the necessary and sufficient conditions to characterize global optimal solution are obtained.
假定约束是可行、规范的，对于目标函数为正定或半正定的情形，得到了全局最优解的充要条件。
Under the cases that the objective function is positive definite or positive semidefinite, the necessary and sufficient conditions to characterize global optimal solution are obtained.
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