Description: 微分进化算法来解决电力系统优化问题的源代码，例子采用的是40个粒子的大规模计算问题-Differential evolution algorithm to solve the optimization problem of power system source code, for example, uses 40 particles of mass calculation Platform: |
Size: 3072 |
Author:Mei Juan |
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Description: 基于MATLAB的差分进化算法优化神经网络的源程序-MATLAB based on the differential evolution algorithm for optimization of neural network source code Platform: |
Size: 1024 |
Author:胡月 |
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Description: 差分进化算法的源代码，编码简单可读，可以直接编译执行-Differential evolution algorithm source code, coding simple and readable, and can directly compile the implementation of Platform: |
Size: 2048 |
Author: |
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Description: 这是用matlab编程的关于微分进化算法的源代码。很详细。-It is used matlab programming on the differential evolution algorithm source code. Very detailed. Platform: |
Size: 8192 |
Author:MML |
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Description: Python实现的差分进化算法源代码
使用Python实现的差分进化算法，引用到Numpy和Scipy，可以支持多核与集群并行计算。使用时只需继承DESolver并定义函数def error_func(self, indiv, *args)作为目标函数即可。-Python implementation of the differential evolution algorithm source code uses the Python implementation of the differential evolution algorithm, a reference to Numpy and Scipy, can support multi-core and parallel computing cluster. Just use the definition of Inheritance DESolver and function def error_func (self, indiv,* args) as the objective function can be. Platform: |
Size: 4096 |
Author:谷琼 |
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Description: Python实现的差分进化算法的源代码,供大家学习参考-Python implementation of the differential evolution algorithm source code for everyone to learn information Platform: |
Size: 18432 |
Author:wang |
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Description: 经典差分进化算法的MATLAB代码 毕业设计专用 每代的收缩代码 又要的请下载 求过-Classical differential evolution algorithm graduated from the MATLAB code design dedicated each generation of contraction of code have to download the begged Platform: |
Size: 2048 |
Author:小风 |
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Description: 基于差分演化算法的源程序，非常好用。能处理多种函数优化问题。-Based on differential evolution algorithm source code, very easy to use. Able to handle a variety of function optimization problems. Platform: |
Size: 8192 |
Author:吴伟林 |
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Description: ：组合差分进化算法CoDE是一新的具有竞争力的算法，但收敛速度和寻优性能仍有待改进。为解决上述问题，提出对
组合差分进化算法CoDE从生成策略和控制参数两个方面进行改进，提出了两种改进的CoDE版本MCoDE和MCoDE—P，并
利用6个典型的测试甬数对改进性能进行检验。结果表明结合了最好个体信息的MCoDE方法能够改善CoDE的寻优性能，
而采用控制参数扩展的MCoDE—P方法却难以达到期望的效果。-: Combined differential evolution algorithm CoDE is a new competitive algorithm, but convergence speed and optimization performance needs to be improved. In order to solve the above problem, the pair differential evolution algorithm CoDE generation strategy and control parameters from the two aspects of improvement, we propose two improved versions MCoDE CoDE and MCoDE-P, and using six typical test Yong Number on improving performance tested. The results show that combines the best individual information MCoDE method can improve CoDE the optimization performance, while the use of the control parameter expansion MCoDE-P method is difficult to achieve the desired effect. Platform: |
Size: 732160 |
Author:asda |
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Description: 文档是关于差分进化的改进算法，通过自适应的改变差分进化的参数，有效地提高了算法的性能，有测试函数和数据，可直接运行-Documentation is on improved differential evolution algorithm, by changing the parameters of the adaptive differential evolution, and effectively improve the performance of the algorithm, there are test functions and data can be run directly Platform: |
Size: 14957568 |
Author:郭洁皓 |
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Search - code Differential evolution algorithm