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heuristic random searching
相关语句
  启发式随机搜索
     This paper proposes combining a heuristic random searching strategy with a local optimization algorithm, and names it Mixed Global Optimization Algorithm (MGOA) to overcome the difficulties.
     文中将启发式随机搜索策略和局部优化算法相结合 ,构造混合全局寻优算法 .
短句来源
     Combining a heuristic random searching strategy with local optimal algorithms is effective solution for complex optimization problem.
     启发式随机搜索策略和局部优化算法相结合的求解方案是解决复杂函数优化的有效途径 .
短句来源
     This paper, combining a heuristic random searching strategy with local optimal algorithm, proposes and develops a composite algorithm named Mixed Global Optimization Algorithm (MGOA) to overcome the difficulties.
     本文将启发式随机搜索策略和局部优化算法相结合 ,构造了混合全局优化算法 (MGOA)来解决这一困难。
短句来源
  “heuristic random searching”译为未确定词的双语例句
     Essentially genetic algorithm is a kind of robust heuristic random searching algorithm used to deal with the complicated problems.
     遗传算法就其本质而言是一种用于处理复杂问题的鲁棒性强的启发式随机搜索算法。
短句来源
  相似匹配句对
     ON RANDOM
     论随机性
短句来源
     Random searching multicast tree generating heuristic
     随机搜索组播树生成算法
短句来源
     On Heuristic Teaching
     启发式教学拾零
短句来源
     random variables.
     随机变量序列完全收敛性及重对数律的精确渐进性质的进一步推广.
短句来源
     Bidirectionally Heuristic Graph Search Algorithm BRA on Random Production System
     随机产生式系统的双向启发式图搜索算法BRA
短句来源
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The control of unmeasurable variables is a constant concern in academia and industies. In this paper, predictive control, inferential control and soft sensor are combined to form a new control strategy for preinferential control of unmeasurable variables, which takes the soft sensor as the predictive model, and a new method of heuristic random search as optimization algorithm. The simulation analysis based upon an industrial separation unit indicates that this strategy has the good regulatory performance...

The control of unmeasurable variables is a constant concern in academia and industies. In this paper, predictive control, inferential control and soft sensor are combined to form a new control strategy for preinferential control of unmeasurable variables, which takes the soft sensor as the predictive model, and a new method of heuristic random search as optimization algorithm. The simulation analysis based upon an industrial separation unit indicates that this strategy has the good regulatory performance of disturbance rejection.

将预测控制、推断控制和软测量三者结合起来,以软测量估计器作为预测模型,以一种新的启发式随机搜索方法作为优化算法,构成一种针对不可测变量的新的控制策略──预推断控制.以实际工业装置为背景的仿真研究表明,这种控制策略能够有效地抑制不可测扰动,从而为解决不可测变量的控制问题提供了一条有效途径.

Primary routing is a main part of the whole procedure of distribution planning. Essentially genetic algorithm is a kind of robust heuristic random searching algorithm used to deal with the complicated problems. In order to obtain an economical. high-effective and low-loss primary route, this paper leads genetic algorithm into primary routing system as a searching mechanism. In the direct view environment of geographic information system. the primary routing system finds an optimal primary route for...

Primary routing is a main part of the whole procedure of distribution planning. Essentially genetic algorithm is a kind of robust heuristic random searching algorithm used to deal with the complicated problems. In order to obtain an economical. high-effective and low-loss primary route, this paper leads genetic algorithm into primary routing system as a searching mechanism. In the direct view environment of geographic information system. the primary routing system finds an optimal primary route for distribution networks by utilizing the geographic data and topological information provided by facility manage system database.

初级布线是整个配网规划过程的一个重要组成部分。遗传算法就其本质而言是一种用于处理复杂问题的鲁棒性强的启发式随机搜索算法。为了得到经济、高效、低耗的初级布线路径,将遗传算法作为一种路径寻优机制引入初级布线系统。该系统在地理信息系统直观的视图环境中,充分利用设备管理系统数据库所提供的地理图形数据和网络拓扑信息,为配网规划寻找到一条最优的初级布线路径。

Packing problems are categorized as NP complete. Traditional optimization methods have difficulties to deal with such problems effectively. Recently, genetic algorithms (GA) and simulation annealing algorithms (SAA) were resorted to, but their efficiency to locate a precise result was not quite satisfactory. This paper proposes combining a heuristic random searching strategy with a local optimization algorithm, and names it Mixed Global Optimization Algorithm (MGOA) to overcome the difficulties. Multi...

Packing problems are categorized as NP complete. Traditional optimization methods have difficulties to deal with such problems effectively. Recently, genetic algorithms (GA) and simulation annealing algorithms (SAA) were resorted to, but their efficiency to locate a precise result was not quite satisfactory. This paper proposes combining a heuristic random searching strategy with a local optimization algorithm, and names it Mixed Global Optimization Algorithm (MGOA) to overcome the difficulties. Multi object optimization model is formulated on a simplified satellite cabin packing problem, and taking its known optimal solution as the criteria of evaluation, MGOA is superior to the Multiplier Algorithm and an Improved GA in term of solution quality and efficiency. Therefore, the proposed MGOA has shown some potential to deal with packing problems with good expectation.

布局问题是 NP完全问题 ,传统的优化算法很难求得全局最优解 ,遗传算法和模拟退火算法等的随机搜索算法的求解精度和效率不能令人满意 .文中将启发式随机搜索策略和局部优化算法相结合 ,构造混合全局寻优算法 .以旋转卫星舱布局问题的简化模型为背景 ,建立了多目标优化的数学模型 ,通过一已知最优解的布局算例与遗传算法和乘子法的计算结果比较 ,该算法求解的质量和效率更优 ,表明此算法在布局优化中具有应用潜力

 
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