搜索资源列表
NSGA
- 工业中，用于换热网络优化的非支配排序遗传算法源程序-In industry, the non dominated sorting genetic algorithm is used to the optimization of the heat exchanger network
NSGA-II(matlab程序)
- 基于多目标优化的遗传算法，非常适合parato逼近运算
NSGA2
- 基于非支配排序的带有精英策略的多目标优化算法:NSGA-II,测试函数是DTLZ-Based on the non-dominated sorting multiobjective optimization algorithm with elitist strategy: NSGA-II, the test function is DTLZ
Application-of-modified-NSGA-II
- optimal reactive is very good paper
1
- code is relate be NSGA-code is relate be NSGAII
NSGA-II
- 拉丁超立方采样matlab程序：在尽可能少的样本点下建立尽可能精确的模型-latin hypercube sample
NSGA-II
- 最经典的遗传算法，各个领域都在这个基础上进行编程。-The classic genetic algorithm
nsga3cpp1.1
- NSGA-III遗传算法，实现超多目标优化-NSGA-III algorithm, ultra-multi-objective optimization
NSGA-II
- MultiObjective Excellent Code
paper3
- A fast and elitist multiobjective genetic algorithm NSGA--A fast and elitist multiobjective genetic algorithm NSGA-II
NSGA-II
- this file is optimal sizing component DG by using algorithm GA
NSGA-2
- NSGA2算法，求解多目标优化函数的典型算法。-NSGA2 algorithm, typical algorithms for solving multi-objective optimization function.
vns-mpso
- 将NSGA-II 的理念融入粒子群中，并加入保存优秀解的机制，提出MPSO算法。针对MPSO 易陷入局部极值的缺点，加入变邻域机制，通过随机的破坏旧解和重建新解，找到全局最优解，以达到帮助粒子跳出局部极值的目的，提 出VNS-MPSO 算法。-Will the NSGA- II concept into particle swarm, and join save good solution mechanism, MPSO algorithm is put forward. Against d
PICEAgMATLAB
- PICEA-g是一种有效智能多目标优化算法，算法性能，特别是在高维多目标优化问题上，优于传统的NSGA-II以及MOEA/D算法。并且算法无需额外的参数设置，简单易用。 -PICEA-g is a competitive multi/many-objective optimizer. Its performance is better or comparable to the state-of-the-art algorithms such as NSGA-II and MOEA/D on
NSGA2-MATLAB-Codes
- nsga-2 实现多目标优化的算例利用简单的二进制编码进行遗传变异运算-nsga-2 An example of multi objective optimization
NSGA-II
- NSGA2求解单目标或者多目标函数的极值，适用于初学NSGA2的学者，采用实数编码方式，中文注释清晰，有图有真相-NSGA2 to solve the single objective or multi objective function of the extreme value, suitable for beginners NSGA2 scholars, using the real number coding method, Chinese Notes clear, there is a
INSGA-II
- 针对NSGA-II算法存在重复个体的问题进行改进INSGA--Improvements INSGA-II for the NSGA-II algorithm duplicate individual problems
NSGA 2程序
- 本算法为一查算法实行多目标优化的程序 可以根据自己问题 修改变量数目 目标数目实现优化
NSGA
- 在open shop情形下，采用遗传算法生成最优调度方案，所得Pareto解释最优的。-In the case of open shop using genetic algorithm to generate the optimal scheduling scheme, the resulting interpretation Pareto optimal.
MGSO
- 利用MGSO算法实现多目标问题，对比算法为NSGA。-Use MGSO algorithm multi-objective problem, comparing algorithm NSGA.