搜索资源列表
NSGA-II
- 演化、遗传计算方法NSGA2的源代码-evolution, genetic method NSGA2 the source code!
MOEA-NSGA-II
- 基于进化算法的多目标优化,以matlab程式所寫成,請大家參考參考-evolutionary algorithm based on the multi-objective optimization to written Matlab program, please refer to reference
nsga-original
- 原始非支配多目标遗传算法，适用于多个目标函数的输入，多个变量的输入，非常经典的，基于非支配解排序。-original non-dominant multi-objective genetic algorithm applied to a number of objective function input, a number of input variables. very classic, based on the non-domin
NSGA(C++) 单目标遗传算法
- 单目标遗传算法c++ 本程序以调通，有充分的说明便于理解 须注明的是本程序来自于网络-single objective of the genetic algorithm c procedures to transfer links. Note fully annotated to facilitate understanding of the process from the network
NSGAII.tar
- NSGA
NSGA-II
- 多目标进化算法，带精英策略的非支配选择遗传算法(multi-objective evolution algorithm)
nsga
- NSGA2优化算法Matlab求解多目标优化问题,欢迎下载学习(NSGA2 optimization algorithm Matlab to solve multi-objective optimization problem, welcome to download and learn)
遗传优化工具箱 - NSGA-II
- 改进的遗传算法，多目标优化算法，简单，快捷有效(Improved Genetic Algorithm)
MOEA-NSGA-II
- MOEA-NSGA-II algorithm
多目标遗传算法matlab程序
- 多目标遗传算法，实现多目标优化,优化结束之后可用topsis方法进行选取(NSGA 2 ,After the optimization can be used topsis method to select)
NSGA2
- nsga-II matlab代码 数值算法/人工智能/matlab例程(Nsga-II matlab code numerical algorithm / AI /matlab routines.)
NSGA-II-master
- 在Python中利用遗传算法进行目标优化的程序代码(Using Genetic Algorithms for Target Optimization in Python)
NSGA-II
- matlab实现的多目标优化遗传算法代码。可输出结果并画图。(The matlab program of NSGA-II.)
NSGA-II
- 利用NSGA-II算法实现水资源配置多目标优化问题(Realization of multi-objective optimization of water resources allocation based on NSGA-II algorithm)
NSGA
- 轻松实现多目标优化，本文件自主设计，MATLAB平台实现(Easy implementation of multi-objective optimization, this document is designed independently.Implementation of MATLAB platform.)
NSGA-II
- 电网优化，能够用于分布式电源的选址定容，也可以用于电网的网架优化计算等等(Power network optimization)
NSGA2。5
- 多目标算法NSGA-2,亲测可用，求解方便，matlab编写，其中测试函数为ZDT1(Multiobjective algorithm NSGA-2, which can be used for pro-test and easy to solve, is compiled by matlab. The test function is ZDT1.)
NSGA
- 多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域
nsga2
- 多目标优化算法NSGA-II的python实现(Python implementation of NSGA-II)
NSGA2 eva
- nsga-2 遗传算法，应用于ieee-33节点系统中，方便快捷，好用。(Nsga-2 genetic algorithm is applied in ieee-33 node system, which is convenient, fast and easy to use.)