optimization [,ɔptimai'zeiʃən]

optimization的基本解释

n.

最佳化, 最优化

与optimization相关的网络例句 [注：此内容来源于网络，仅供参考]

The traditional query tree optimization methods,parallel database optimization methods based on left linear trees and right linear trees,bushy trees,and operation of the forest,have their own pros and cons,they have been more in-depth and maturity of the study.The query optimization method based on multiple weighted tree has studied its model of parallel query plan,its complexity model of parallel query plan and query optimization algorithms.The semantic query method transforms an inquiry into one or several semantic equivalence inquiries then has to find and implement a strategy to achieve a better query.Agent-based parallel database query optimization using Multi-Agent technology to automatically search the integrity constraint conditions which are related to the determined query,there for,the efficiency between several relations' joins has been greatly improved.The parallel optimization algorithm,based on genetic algorithm which is suitable for multi-joins of cluster environment,has deeply studied the relations storage options,multi-joins query optimization and query processing and other key technologies based on cluster parallel database.

传统的查询树优化方法，即基于左线性树、右线性树、浓密树、操作森林的并行数据库查询优化方法，各有优劣，对其的研究比较深入、成熟；基于多重加权树的查询优化方法，研究了其并行查询计划模型、并行查询计划的复杂性模型和查询优化算法；语义查询优化方法将一个查询变换成一个或数个语义等价的查询，进而寻找并执行这些等价查询中具有较好实现策略的一个；基于Agent的并行数据库查询优化采用Multi-Agent技术自动查找与给定查询有关的完整性约束条件，使得多个关系间连接操作的效率得到很大的提高；基于遗传算法的并行优化算法，深入研究了基于机群并行数据库中关系存储的选择、多连接查询优化和查询处理等关键技术。

There exits several MDO algorithms. But they are in our opinion either nonefficient or complicated. So we devised an algorithm called Subspace Approximation Optimization . In the SAO algorithm, the whole system is decomposed into one system-level optimization and several disciplinary optimizations so that a large and complicated problem can be divided into several easy-solving sub-problems. The coupling relationships and the coordination among disciplines are presented by equality constraints and these equality constraints are assigned to relevant disciplines. The optimums of design variables in system level optimization are transferred to discipline level optimization. The optimums of design variables in discipline level correspond to the point that is the nearest to the optimums of design variables in system level. If the optimums of design variables in system level are out of feasible region of discipline 1eve1, linear constraints can be built in the system level optimization using the design variable optimums obtained by the discipline level optimization. The system level optimization would improve the design of the whole system with these linear constraints.

目前，国内外已经发展出了多种飞机多学科设计优化算法，本文的重点是针对协同优化算法的不足，提出了子空间近似优化算法(Subspace ApproximatingOptimization，SAO)，SAO算法中，整个系统的优化问题被分解成一个系统级优化和若干学科级优化，而各个学科之间的耦合与权衡关系则被当作等式约束，这些等式约束将被分配到各个学科级优化中去，系统级优化的任务是寻找整个系统的最优解，而学科级优化的目标函数是以系统级优化分配下来的设计点为圆心的超球半径的平方，因此，如果系统级优化分配下来的设计点在学科级优化可行域内，则学科级优化目标函数为0，反之，则学科级优化的最优点是系统级优化当前设计点距离可行域最近的点。

A fine-grained genetic algorithm based algorithm which is for the parameters optimization problem of agent model is proposed. The fine-grained genetic algorithm is used to solve the parameters optimization problem of agent model. As to the parameters optimization problem of the agents colony model, the first step is to define the concept of "the parameters mode of the combat agents colony" to describe the parameters of the combat agent colony model. Then the genetic algorithm is used to solve the optimization problem with the parameters mode of the combat agent colony model as the genetic individual. Besides the algorithm, the algorithm dispatch tactic is also considered. And the two level sequence dispatch tactic and the two level nesting dispatch tactic are put forward for those complex parameters optimization problem. At last, the model parameters optimization under antagonism codition is studied. The means to solve this problem is to translate it into a kind of antagonism problem, then use the competitive co-evolutionary genetic algorithms to solve it.

其中：提出了一种基于细粒度模型的作战智能体模型参数优化求解算法，用细粒度模型遗传算法求解作战智能体模型参数优化问题；提出了"作战智能体群体参数模式"的概念，对作战智能体群体模型参数进行形式化描述，再以作战智能体群体参数模式为遗传个体，应用遗传算法求解作战智能体群体模型参数的优化问题；研究了上述优化问题求解算法的调度策略，提出了分层模型参数优化算法的双层顺序调度策略和双层嵌套调度策略，解决复杂的模型参数优化问题；将对抗条件下作战模型参数的优化问题转化为一种对抗性问题，应用竞争性共同进化遗传算法，进行求解。

与optimization相关的网络解释 [注：此内容来源于网络，仅供参考]

optimization：优化

内省机制在存储系统普通的运行过程中增加了"观测"(observation)和"优化"(optimization)两个部分. 观测模块监测并记录系统的运行状况,之后从所有记录的历史数据中提取有用的特征进行分析. 优化模块根据这些分析结果调整系统的运行参数和各种策略的选择以适应环境的变化.

optimization：最优化

今天WORKSHOP时老师总结了一下我们学校的运筹学的3大优势即预测学(FORECASTING), 最优化(OPTIMIZATION) 和仿真学(SIMULATION). 所以呀,学好了运筹学就要会算命(利用一些历史数据来看未来就象看生辰8字一样,科学算命也许更贴切),

optimization：最佳化

进化计算以观察自然界生物与生命现象之动态变化为想法出发,开发符合自然状态之进化模拟演算法,可应用於搜寻 (Search)、设计 (Design)、与最佳化 (Optimization) 等各类问题解决之演算方法或解题技术,可应用的范围非常广泛.

optimization：最适化

假设一个具有相关的所有变数的状态空间(state space),给与可设定的策略集群,制定出一个以上的最适基准或适应度函数的方法. 在最适化(optimization)理论中,因为如何确定效率的问题以及在个体理论的观点上对有的依存关系不易处理,所以留下不少难解之点.

global optimization：全局优化

过去人们主要通过全局优化(Global Optimization)的方法(密度泛函,蒙特卡洛和遗传算法等)致力于寻找基态结构,但是这些方法有一个明显的问题,最后的结果严重依赖于体系所赋予的初始构型,而且在结构的研究中过去