解空间

基于可满足解空间的DNA算法&解决最小顶点覆盖问题

A DNA Algorithm Based on Satisfiable Solution Space & for the Minimum Vertex Cover Problem

巨型柔性Stewart平台解空间的研究

A study of solution space for huge flexible Stewart Platform

协同DNA计算机解空间问题的模块化设计方案

Modularization Scheme for Solving Solution Space Detection Problem of DNA Computer with Coprocessor

多目标优化（Multi-ObjectiveOptimization，MO）算法的目的是在解空间中找到一组最优的，互不支配的，且分布均匀的解。

Multi-objective optimization ( MO ) algorithm is to find a set of nondominated and well distributed solutions .

提出了一种基于检测型生物芯片的协同DNA计算机解空间问题的模块化解决方案。

This paper presents a new modularization scheme for solving the solution space detection problem of DNA computer with coprocessor by using biochip for detection .

曝光时间和CR摄影得分为目标的0-1整数规划模型，用分组过滤方法简化解空间，最后用遗传算法求解。

Grouped filtering method was used to simplify the solution space , then the optimized model is solved by genetic algorithm .

在此基础上，MDA为从问题空间到解空间的自动映射提供了理论上的支持，使得Web应用的代码自动生成成为了现实。

In addition , MDA gave theoretical support to the mapping from problem space to solution space , which made automatic codes generation of web applications come true .

通过使用遗传算法对神经网络的初始权值进行优化，可以在解空间中定位出一个较好的搜索空间，然后采用BP算法在这个小的解空间中搜索出最优解。

Through the use of genetic algorithms to optimize original weights and biases of neural network , a better searching space in solution space could be obtained .

遗传算法由于其隐含并行性和全局解空间搜索两大优点而成为解决Jobshop问题的常用工具。

Because of its implicit parallelism and global searching ability , Genetic Algorithm ( GA ) becomes the widely used Algorithm in resolving Job Shop Scheduling Problems ( JSP ) .

该模型首先将问题解空间的DNA分子固定在固体载体上，然后通过进行相应的生化反应来求得哈密顿回路问题的所有解。

The DNA molecules of the solution space are fixed on the solid carrier , and then we get the all solutions of the perfect matching problem by the biochemical actions .

多重R－H法及二重Ernst方程解空间的无穷维对称Virasoro群

Multiple R-H Method and the infinite - Dimensional Virasoro Group on the Solution Space of the Double Ernst Equation

巨型柔性Stewart平台解空间、工作空间的研究及悬索张力的优化分析

Study on solution space , working space and cable tension 's optimized analysis for huge flexible Stewart Platform

NP问题的解空间太大导致利用现有技术求解十分困难。

It is very difficult to solute NP problem with current technology because the set of feasible solutions is very huge .

解空间Riesz分数阶扩散方程的一种数值方法

A numerical method for the space Riesz fractional diffusion equation

AVO反演是一个非线性优化问题，因此采用非线性的反演方法，其解空间的性质、状态应优于线性反演方法。

P-wave AVO inversion is a complicated constrained nonlinear optimization problem , which is better achieved using global optimization methods .

基于解空间分解的GMRES算法及其在图像处理中的应用

GMRES Algorithm Based on Decomposition in Solution Space and its Application in Image Restoration

上层GA通过树基对解空间进行划分，缩小搜索范围，同时通过改良的遗传操作，减少不可行解出现的几率；

In the upper module , GA can reduce hunting space by the way of dividing solution space , additionally , an improved GA is developed to reduce unfeasible solutions ;

在解空间，设计模式和框架的研究得到越来越多的关注，尤其是MVC结构的框架的日渐成熟促使Web应用开发发生革命性的突破。

In solution space , design patterns and frameworks attracted more and more attention . With the maturation of the MVC structure framework , people have made great breakthrough in the development of web applications .

提出多子群辅助的PSO算法，兼顾了对解空间的全局搜索和局部开发。

Furthermore , multi-subgroup assistant particle swarm optimization ( MSA-PSO ) is proposed , in which the information exchange between a dominant group and the subgroups is introduced .

由网格服务组成的工作流（GSF）的调度问题是一个典型的NP问题，由于遗传算法具有并行性和全局解空间搜索的特点，非常适合解决这个问题。

As an NP problem , grid service scheduling is difficult to be solved by means of classic algorithms .

遗传算法（GeneticAlgorithms简写GA）是用于优化问题的新的有用的工具，其运用了自然界中优胜劣汰的法则，并且在可能的解空间上形成多点逼近的评价工具。

Genetic Algorithms ( GA ) are new techniques useful for optimization problems , in which the law of ' natural selection ' is employed , and it is an evaluating tool to form multi-interpolation in possible solution vectors .

本文基于Adleman模型的生物操作与粘贴模型的解空间，提出的一种求解最小生成树问题的DNA计算机新算法。

We propose a DNA computing algorithm for minimum spanning tree on the basis of biological operation in Adleman model and solution space in sticker model .

但是，该算法的本身特性决定了算法不趋向于搜索接近极值点的解空间，造成了PSO算法最终解的局部极值性不好；

However , the PSO algorithm does not search the solution space of the points closest to the extrema , which leads to the bad local extrema of the final solutions .

以广义解空间为工具，研究抽象柯西问题与积分C半群的关系.证明了ACPk+1存在唯一的解对应A有一k次积分C半群；

By using the generalized solution space , we investigate the abstract Cauchy problem and integrated C-semigroup s , and prove that the existence of a unique solution of ACP_ ( k + 1 ) is equivalent to A having a k-times integrated C-semigroup ;

超分辨率问题是一个病态反问题（ill-posedinverseproblem），具有无限的解空间；其求解过程往往会陷入局部极值，导致结果不尽理想。

But the super-resolution problem is an ill-posed inverse problem . The solution is often trapped in local extremum , thus leading to unlimited solution space and increasing the time complexity . The result is often less than ideal .

该方法分两部分，首先改进原HTN的分解方式，使其在规划过程中能同时搜索更多的解空间，以提供给用户更多的可行组合方案。

First , improve the decomposing way in HTN to search more solutions to provide users more feasible composition plans .

PSO是一种基于群体智能的优化算法，算法简洁，易于实现，能够实现在复杂的解空间中寻找最优解的目的。

The PSO is a kind of evolutive calculational methods based on group brainpower . This algorithm is straightforward and easy to be implemented , and can obtain the optimal solution in the complicated spatial .

利用信息系统的优劣关系对解空间进行划分，形成了在Pareto优势空间进行开采而在其余空间中探索的进化策略。

The solution space can be divided by the dominance relation of the information system and forms the evolution strategy which mines in the Pareto dominance space and searches in the rest space .

通过对问题解空间结构的深入分析，得到了3-SetPacking问题的求解与问题两个特殊实例的求解存在着密切的联系。

By further analyzing solution structure , the solving of 3-Set Packing problem is closely related to the solving of two special problem instances .

利用Matlab编写了点对点分析计算薄膜光学常数和厚度的蚁群算法，并对其进行了优化，使程序能够高效率的实现对解空间的搜索并提高了程序寻找全局最优解的能力。

A point-to-point fitting program based on Matlab was written by using the ant colony algorithm . We have optimized the program to make it effective when searching the solution space and improve its ability to search the global optimal solution . 4 .