搜索资源列表
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
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
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-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
NSGA-II
- 非支配排序遗传算法MATLAB代码实例，用于非支配排序遗传算法优化-Non-dominated Sorting Genetic Algorithm MATLAB code examples for non-dominated sorting genetic algorithm optimization
GAgaijin
- 该程序是基于非支配排序遗传算法改编的实用型算法，该算法收敛速度快而且能够避免收敛在局部最优非常好用-The program is based on the practical non-dominated Sorting Genetic Algorithm algorithm adapted the algorithm converges faster and can avoid local optimum convergence in very handy
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)
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-II
- 本程序是关于基于非支配排序遗传算法2的matlab程序，用于求解多目标优化问题的非支配解。(The non-dominated solutions of multi-objective optimization problems)
Constrained NSGA2
- 添加了了约束的非支配排序遗传算法的代码。(A structure MATLAB implementation of NSGA-II for Evolutionary Multi-Objective Optimization)
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-II
- 非支配排序的遗传算法matlab实现，pareto原理求解多目标问题(Matlab implementation of nsga2 with non dominated sorting and Pareto principle to solve multi-objective problems)
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