搜索资源列表
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-dominant solution ranking.
nsga_2
- 基于非支配排序遗传算法处理多目标优化的matlab例程,可以自行修改-Based on NSGA handling multi-objective optimization of Matlab routines, can make its own decisions
nsga2_c_source
- 用C++语言写的非支配排序遗传算法的例子，顶！-Written in C++, the non-dominated sorting genetic algorithm for example, the top!
NSGA
- nsga2非支配排序遗传算法，c++源码实现-nsga2 non-dominated sorting genetic algorithm, c++ source code to achieve
mo-nsga-deb
- 改进的非支配排序遗传算法，求解多目标问题-non dominated sorted algorithm II for multiple objective problem
NSGA2-based-function-optimization
- NSGA2是一种快速非支配排序的经典遗传算法，我们利用NSGA2对函数进行优化。-NSGA2-based function optimization.
code
- 带精英策略的非支配排序遗传算法，c源代码。-This is a GA implementation for multi-objective optimization. For multi-objective optimization, non-domonated sorting has been used.
NSGA-IIa
- 这是prof.kalyanmoy deb 和他的学生开发的非支配排序遗传算法。-This program is the implementation of the NSGA-2 proposed by Prof. Kalyanmoy Deb and his students .
naga2.file
- 带精英策略非支配排序遗传算法的应用实例。-The control strategy with elite sort of genetic algorithm, the application example.
NSGA-II
- 多目标无功优化算法，采用遗传算法，非支配排序，前推回代潮流计算-Multi-objective reactive power optimization
MOEA-NSGA-II.zip
- 多目标的经典遗传算法MOEA-NSGA-II，采用平衡集与非支配排序方法,Classic multi-objective genetic algorithm MOEA-NSGA-II, using the non-dominated sorting method
NSGA-II
- matlab环境下实现的非支配排序遗传算法（NSGA-II），该算法在快速找到Pareto前沿和保持种群多样性方面都有很好的效果-matlab environment to achieve non-dominated Sorting Genetic Algorithm (NSGA-II), the algorithm quickly find the Pareto frontier and maintaining the diversity of the population has a goo
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-II2
- 基于非支配选择的遗传算法（NSGA-II），可用于求解多目标问题，并给出给定数目的帕累托前沿-Based on the non-dominant selection genetic algorithm (NSGA-II), can be used to solve the multi-objective questions and a given number of Pareto frontier
NSGA-II
- NSGA-II非支配排序遗传算法，可避免陷入局部最优，属于全局算法-NSGA-II non dominated sorting genetic algorithm, can avoid falling into local optimal, belong to the global algorithm
gamultiobj
- 基于遗传算法的多目标优化算法，包括支配于非劣，序值与前端，拥挤距离，最优前端个体系数等概念(The multi-objective optimization algorithm based on genetic algorithm includes the concepts of dominating and non inferiority,rank and front , crowding distance and ParetoFraction)
NSGA-II
- NSGA2 带精英策略的非支配遗传算法 matlab算法包(NSGA2 elitist genetic algorithm matlab algorithm package with elitist strategy)
非支配排序遗传算法
- 非支配排序遗传算法NAGS2，算法程序进行了相应的注释，可以运行(Non dominated sorting genetic algorithm NAGS2, algorithm procedures for the corresponding notes, you can run)
NSGA-II
- NSGA是基于对个体的几层分级实现的。在选择执行 前,群体根据支配与非支配关系来排序:所有非支配个体被排成一类,这些被分级的个体共享它们的虚拟适应度值。然 后,忽略这组已分级的个体,对种群中的其它个体按照支配与非支配关系再进行分级,该过程继续直到群体中的所有个体被分级。(The NSGA is based on the individual layers of grading. Before selecting execution groups, according to govern with
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域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性