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chaos-genetic algorithm
相关语句
  混沌遗传算法
     An improved chaos-genetic algorithm with faster evolution speed and better precision is brought forward for it.
     同时,还提出了具有快速收敛和高计算精度的改进混沌遗传算法,并将其应用于购电优化计算中。
短句来源
  “chaos-genetic algorithm”译为未确定词的双语例句
     Inversion of Lithology Parameter Contrasts Based on Chaos-Genetic Algorithm
     用混沌遗传优化方法反演岩性参数变化率的研究
短句来源
     Based on this,this paper does intelligent integrate of Genetic Algorithm and Chaotic Optimization methods and supplies a integrated Chaos-Genetic Algorithm(CGA)by analyzing and researching Genetic Algorithm and Chaotic Optimization methods on theory mechanism.
     混沌的遍历性、随机性和内在规律性使得混沌优化能够互补地与遗传算法进行集成。 基于此,该文经过遗传算法和混沌优化方法的理论机制分析,将二者进行智能集成,给出混沌遗传优化算法CGA。
短句来源
  相似匹配句对
     Genetic Algorithm in Chaos
     混沌遗传算法(英文)
短句来源
     Genetic Algorithm
     遗传算法
短句来源
     Chaos genetic algorithm and its application in test generation
     一种混沌遗传算法及其在测试生成中的应用
短句来源
     Optimizing Complex Functions by Chaos Genetic Algorithm
     复杂函数优化的混沌遗传算法
短句来源
     CHAOS GENETIC OPTIMAL ALGORITHM AND DESIGN OF ITS PROGRAM
     混沌遗传优化算法及其程序研制
短句来源
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By introducing chaos state to genetic algorithm,this paper proposed a novel optimization algorithm named chaos genetic algorithm.Its major measures are chaos initialization and chaos carrier wave of best fitness in population both based on the ergodicity,stochastic property and regularity of chaos.Simulation results of typical complex function optimization show that chaos genetic algorithm improves the convergence and reduces the computing time greatly.

将混沌融入遗传算法提出了混沌遗传算法 ,该方法利用混沌运动的随机性、遍历性、对初始条件的敏感性等特性进行群体的混沌初始化和最优个体的混沌变尺度载波寻优 .典型复杂函数优化的仿真结果表明该方法较遗传算法具有更快的收敛速度和更小的计算量 ,是复杂函数优化的有效手段 .

By the full use of the ergodic property of chaos movement, a Chaos Genetic Algorithm(CGA) is proposed in this paper. The basic principle of CGA is that the small disturbance is added to child generation group by using the chaos variable and the disturbance extent is adjusted little by little as the search is going on. The computation results indicate that the CGA has good performance and significantly improves the computational efficiency in optimization.

本文利用混沌运动的遍历性 ,提出了一种求解优化问题的混沌遗传算法 ( ChaosGenetic Algorithm,简称 CGA) ,该算法的基本思想是把混沌变量加载于遗传算法的变量群体中 ,利用混沌变量对子代群体进行微小扰动并随着搜索过程的进行逐渐调整扰动幅度。研究结果表明 ,该方法效果显著 ,明显提高了优化计算效率。

By use of the ergodic property of chaos movement and the inversion of genetic algorithm, a chaos genetic algorithm(CGA) was proposed. Its basic principle lies in the small disturbance of which extent is adjusted during searching to child generation group using the chaos variable. The results indicate that the CGA has good performance and significantly improve the computational efficiency in optimization compared with others. The optimization steps are as follows in slag making period and copper...

By use of the ergodic property of chaos movement and the inversion of genetic algorithm, a chaos genetic algorithm(CGA) was proposed. Its basic principle lies in the small disturbance of which extent is adjusted during searching to child generation group using the chaos variable. The results indicate that the CGA has good performance and significantly improve the computational efficiency in optimization compared with others. The optimization steps are as follows in slag making period and copper making period of copper smelting converter: sample collection, data pretreatment, space transformation using PLS(Partial Least Squares), modeling using BPN and optimization using CGA. The function of degree of adaptability is f(T)=(-|T-1?250|) max  in slag making period and the function of degree of adaptability is f(T)=(-|T-1?180|) max  in copper making period. The operation parameter variables are changed within the limits of ±10% in the scope of training samples. The copper productivity was improved by 6%, the mass of cold input was increased by 8% and the average converter life span was improved from 213 to 235. The economic profit reaches 26.4 million RMB produced by the increasing productivity of coarse copper.

利用混沌优化的遍历性和遗传算法优化的反演性 ,提出了混沌遗传算法 (CGA) ,其基本思想是把混沌变量加载于遗传算法的变量群体中 ,利用混沌变量对子代群体进行微小扰动 ,随着搜索过程的进行逐渐调整扰动幅度。结果表明 ,该方法优化效果与前人的优化结果相比 ,优化效率明显提高。由炼铜转炉造渣期与造铜期操作参数 (样本采集、数据预处理、PLS(偏最小二乘法 )空间变换、BPN神经网络建模及CGA)的优化和造渣期适应度函数与造铜期适应度函数的变换 ,使操作参数变量在训练集给出的数据范围的基础上延伸± 10 % ,得到最优点对应的工艺条件 ,并用于生产中。经过 3个多月的试运行 ,粗铜产量提高 6 .0 % ,冷料处理量提高 8% ,平均炉寿从原来的 2 13炉提高到 2 35炉

 
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