搜索资源列表
nsga2_Matlab_xixilee
- 对多目标优化算法NSGA-II算法的改进，该算法进化代数少，但是获得的最终效果特别好！-pair of multi-objective optimization algorithm NSGA-II algorithm, the algorithm evolutionary less algebra, However, the ultimate effect was particularly good!
NSGA-II多目标规划代码（matlab版）
- 印度Deb教授开发的Non-dominating Sorting Genetic Algorithm的改进版本，非常适合parato封面逼近运算。
NSGAII
- 实现了NSGA-II，有仿真图和结果,说明了改进算法的优势-achieve NSGA-II，there are graph and results，so the method is more good.
NSGA-II
- MATLAB的NSGA改进后的算法-MATLAB' s NSGA improved algorithm
NSGA-II
- NSGA-II源程序,用于求解多目标优化问题，是对NSGA在非支配集构造方法，维持解分布性策略等方面进行的改进。-NSGA-II source code for multi-objective optimization problem, NSGA is a non-dominated set in the constructor, maintenance solutions and other aspects of the distribution strategy of improvement
NSGA2-dynamic
- 多目标优化进化算法目前公认效果收敛性最好的算法NSGA2c++源码,具有一般性，可在此基础上继续改进，对实现其他多目标优化算法很有帮助.-Multi-objective optimization evolutionary algorithm is currently the best recognized effect of convergence of the algorithm NSGA2c++ Source, with the general, could be on this basis
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
NSGA-II
- nsga-2算法实现多目标优化,改进了nsga算法。是一种先进的遗传算法-nsga-2 multi-objective optimization algorithm, improved nsga algorithm. Is an advanced genetic algorithm
NSGA-II
- 针对现有改进和声搜索算法(IHS) 的不足，提出一种自适应和声粒子群搜索算法(AHSPSO).-For the purpose of avoiding the disadvantage of improved harmony search (IHS) algorithm, an adaptive harmony search-particle swarm optimization (AHSPSO) algorithm is presented.
MOEA-NSGA-II
- NSGA算法是遗传算法在多目标优化上的实现，NSGA-2则是NSGA算法的改进算法，该程序包能实现NSGA-2的多目标优化-NSGA algorithm is a genetic algorithm on a multi-objective optimization, NSGA-2 is the improved algorithm NSGA algorithm, the package can achieve multi-objective optimization NSGA-2
Advance-NSGA-II
- 改进的NSGA-II的简单例子，包含NSGA-II的基本思想，直接在matlab中就可以运行。-Improve the NSGA- II simple examples, including the NSGA- II, the basic idea of directly can run in matlab.
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
NSGA2
- NSGA2主要是对NSGA算法的改进。NSGA是N. Srinivas 和 K. Deb在1995年发表的一篇名为《Multiobjective function optimization using nondominated sorting genetic algorithms》的论文中提出的。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-Multiobjective function optimization using nondominated sorting gen
Improved-NSGA-II-Optimization
- 对NSGA-II算法进行改进，并应用于柔性车间调度的多目标优化问题-Of NSGA-II algorithm was improved and applied to flexible shop scheduling multi-objective optimization problem
毕业论文《NSGA—II的改进算法研究》
- 是一个对多目标遗传算法进行改进的硕士论文，比较有价值。(It is a master's thesis to improve the multiobjective genetic algorithm, which is of great value.)
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)
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
- 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 algorithm is simply called: NSGA-III.
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