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寻优性能
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  searching performance
    The test results on IEEE 16-bus, IEEE 33-bus and IEEE 69-bus distribution networks show the prominent efficiency and significant global optima searching performance of HGAPSO.
    通过对IEEE16节点、IEEE33节点、IEEE69节点测试系统的计算和分析表明,该方法在解决配电网络重构问题上具有很高的搜索效率和寻优性能
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    According to the advanced searching ability of chaotic variables, overlapping agents with poor optima searching ability are transformed into chaotic swarms while the other continue their PSO process in order to improve the method' s global searching performance.
    该方法结合混沌变量良好的遍历特性及混沌优化的特点,对即将重合而引起搜索能力下降的粒子赋予混沌状态搜索,其余粒子仍以常规PSO方法搜索,从而提高PSO方法的寻优性能
短句来源
    The test shows that the antibody clone algorithm has the prominent efficiency and significant global optima searching performance,and can be effectively used to reconfigure power distribution network for making loading balance.
    算例表明本算法具有很高的搜索效率和寻优性能,可有效地应用于以负荷均衡为目标的配电网络重构。
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  “寻优性能”译为未确定词的双语例句
    The proposed method, which combines the superiority in both genetic algorithm (GA)and particle swarm optimization (PSO), displays more excellent performance than GA and PSO.
    该方法结合了遗传算法(GA)和粒子群优化算法(PSO)两者的优点,体现出较GA和PSO更好的寻优性能
短句来源
    The distribution reconfiguration problem is a non-linear, multi-constraints, mixed-integer optimization problem. Though so many optimal techniques, Genetic Algorithm is applied widely in distribution reconfiguration algorithm for its outstanding performance of search optimization.
    基于网损最小的配网重构问题是一个典型的非线性、多约束的整数组合优化问题,在众多的优化技术中,由于遗传算法有良好的全局寻优性能,在重构算法中得到比较广泛的应用。
短句来源
    So GA was improved and combined with the SA and SQP to form a hybrid genetic algorithm.
    为提高混合算法中所涉及的遗传算法的寻优性能,并结合高温超导磁体优化过程中的实际问题,本文对遗传算法做了很多改进。
短句来源
    This method was proved to have a strong search ability and a fast convergence speed through the test function and the optimizations of the HTS magnets.
    通过测试函数及高温超导磁体优化的结果,证明了该混合算法具有很好的寻优性能和收敛速度。
短句来源
    Theexamples tested indicate that no matter the performance of seeking excellent point orcomputation speed, the reactive power optimization of electrical distribution systembased on adapted immune algorithm is superior to that based on genetic algorithm andgeneral immune algorithm.
    算例表明,基于这种自适应免疫算法开发的无功优化程序无论在寻优性能,还是计算速度上均优于基于目前广泛使用的遗传算法和一般的免疫算法所开发的无功优化程序。
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  searching performance
Searching performance of a real-coded genetic algorithm using biased probability distribution functions and mutation
      
In this article, we present a method of improving the searching performance of the RGA.
      
The aim is to study the effect of the page capacity on searching performance.
      
This behaviour improves the global searching performance.
      
Besides the improved searching performance, SEOM also provides additional features that were not provided in current XML accessing techniques.
      
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This paper proposes an improved solution for distribution network reconfiguration based on a refined hybrid genetic algorithm. In the algorithm, the "the integer permutation" encoding is adopted with each integer representing one controllable switch. A decoder is designed to decide the final network configuration corresponding every chromosome. A local search operator is combined with the genetic algorithm which improves the local optimal capability of the algorithm. The computational results on a tested...

This paper proposes an improved solution for distribution network reconfiguration based on a refined hybrid genetic algorithm. In the algorithm, the "the integer permutation" encoding is adopted with each integer representing one controllable switch. A decoder is designed to decide the final network configuration corresponding every chromosome. A local search operator is combined with the genetic algorithm which improves the local optimal capability of the algorithm. The computational results on a tested system demonstrate the algorithm is feasible and efficient.

提出了一种基于改进的混合遗传算法的配电网重构算法,在算法中使用可操作开关支路的整数编号的排列顺序来表示染色体,并通过译码器的设计来映射染色体所对应的辐射状网络结构,避免了产生不可行解的情况,大大提高了算法的运算效率。同时在算法中引入了局部寻优算子,改善了算法的局部寻优性能。算例结果表明本算法是高效可行的。

A new model of Short-Term Load Forecasting (STLF) based on the fusion of Phase Space Reconstruction Theory(PSRT) and Improved Chaotic Neural Networks(ICNN) is presented in this paper. The ICNN model has strong sensitivity to the initial load value and to the walking of the whole chaotic track. And it can characterize the complicated dynamics behaviors and has global searching optimal ability. The input dimension of ICNN is decided by PSRT, and the training samples are formed by means of the stepping dynamic...

A new model of Short-Term Load Forecasting (STLF) based on the fusion of Phase Space Reconstruction Theory(PSRT) and Improved Chaotic Neural Networks(ICNN) is presented in this paper. The ICNN model has strong sensitivity to the initial load value and to the walking of the whole chaotic track. And it can characterize the complicated dynamics behaviors and has global searching optimal ability. The input dimension of ICNN is decided by PSRT, and the training samples are formed by means of the stepping dynamic space track and nearest neighbor point set of the forecasting phase points. So it can enhance associative memory and generalization ability of forecasting model. The learning algorithm of ICNN adopts genetic algorithm. Two kinds of load systems are used to simulate, and the testing results show that the proposed model can improve effectively and stably the precision of STLF and possesses a good adaptability in the weekday and weekend. This research acquires the effective theoretic progress in the practical prediction engineering.

该文首次提出基于PSRT和ICNN融合的电力系统STLF模型,所构造的ICNN预测模型对负荷初值和混沌轨迹的游动性有很强的敏感性,可表征复杂的动力学行为和具有全局寻优的性能,以PSRT确定ICNN输入维数,训练样本集按预测相点步进动态相轨迹和最近邻点集原理形成的,可增强预测模型对混沌动力学的联想和泛化推理能力;文中用遗传算法作为ICNN的学习算法,对两类不同负荷系统日、周预测仿真测试,证实所研究的预测模型能有效、稳定的提高预测精度,且有较高的适应能力,为将基于PSRT和ICNN融合的电力系统STLF方法用于实际运行系统在理论上取得了有效的进展。

For dynamic reactive power optimization, load variation should be taken into consideration under the constraint of maximal allowable daily operating times. It is a complex nonlinear programming problem. A novel concept, named optimal matching flow for minimal energy loss, is presented in this paper. Based on this concept, a fast algorithm is developed to determine optimal size of capacitor at different time. This approach can calculate the optimal size of capacitors at every time in one calculation process,...

For dynamic reactive power optimization, load variation should be taken into consideration under the constraint of maximal allowable daily operating times. It is a complex nonlinear programming problem. A novel concept, named optimal matching flow for minimal energy loss, is presented in this paper. Based on this concept, a fast algorithm is developed to determine optimal size of capacitor at different time. This approach can calculate the optimal size of capacitors at every time in one calculation process, so it is very efficient. To optimize devices’ switching time, an efficient heuristics based method is also proposed. Results from numerical examples prove this algorithm is practical and efficient.

无功补偿动态优化一方面要考虑设备的最大动作次数约束及优化动作时间;另一方面要根据负荷变化确定不同时段的最优投运容量。这是一个复杂的非线性优化问题。该文基于最优匹配注入流的基本思路,提出了基于能量损耗最小的最优匹配注入流的算法。该算法仅计算一次就可以求出所有电容器在各个时段的最优投运容量,而且具有很好的收敛性和寻优性能;通过利用计算各个时段的静态最优补偿容量和时段的等值融合,提出了一种有效的启发式算法以用于优化电容器的动作时刻。算例表明,文中提出的算法具有良好的收敛性和优化性能,可以应用于实际系统。

 
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