搜索资源列表
NSGA-III
- multi objective function genetic algorithm
A-steady-state-decomposition-based-quantum-geneti
- Document which describes to Nondominated Sorting Genetic Algorithm III (NSGA III) written by Ray-Document which describes to Nondominated Sorting Genetic Algorithm III (NSGA III) written by Ray
nsga3cpp1.1
- NSGA-III遗传算法，实现超多目标优化-NSGA-III algorithm, ultra-multi-objective optimization
MOEAFramework-master
- 多目标进化算法框架，包括MOEAD、NSGA2等经典多目标进化算法。-The MOEA fr a mework is a free and open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose multiobjec
NSGA-III
- 实现对多目标问题的求解，尤其对大规模的多目标问题的求解，效果比较好(An Evolutionary Many-Objective Optimization Algorithm Using Reference-point Based Non-dominated Sorting Approach, Part I: Solving Problems with Box Constraints)
ypea126-nsga-iii
- 包含约束的非支配排序遗传算法3的代码。。(Code of non dominated sorting genetic algorithm 3 containing constraints.)
AirFlowFormulas
- Jan and Deb, extended the well-know NSGA-II to deal with many-objective optimization problem, using a reference point approach, with non-dominated sorting mechanism. The newly developed algorithm is simply called: NSGA-I
NSGA-III
- 用matlab实现的多目标进化NSGA-3算法，直接修改目标函数后即可使用，十分有用(The multi-objective evolutionary NSGA-3 algorithm implemented by Matlab can be used directly after modifying the objective function, which is very useful.)
A-NSGA-III
- 新版改进NSGA算法A-NSGA-III，用于软件仿真平台对比于MOEA/D,PSO等多种算法。(The new version of improved NSGA algorithm a-nsga-iii is used for software simulation platform comparison with MOEA/D,PSO and other algorithms.)
NSGA-III
- 测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并
NSGA-III
- 一种改进的适用于高维的进化算法，采用参考点等方法。(evolutionary algorithm)
NSGA-III
- 新一代nsga算法源代码，其中包含详细的代码及说明。(Jan and Deb, extended the well-know NSGA-II to deal with many-objective optimization problem, using a reference point approach, with non-dominated sorting mechanism. The newly developed algorit