搜索资源列表
NSGA-II
- MATLAB的NSGA改进后的算法-MATLAB' s NSGA improved algorithm
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.
nsga2-gnuplot-v1.1.6
- improved NSGA 2 based optimization code
nsga2-gnuplot-v1.1
- improved NSGA ii code
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
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
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)
煤矿节能减排多目标优化研究
- 针对传统煤矿节能减排优化模型选取的目标函数比较单一的问题,构建了涵盖经济效益、能源消耗、污染物排放量等目标函数的煤矿节能减排多目标优化模型,并应用基于改进的蝙蝠算法寻找3个目标函数之间的优化解,实现了经济效益最大化、能源消耗最低化、污染物排放量最少化的优化结果。仿真结果表明,相比于PSO-E、NSGA-II算法,改进的蝙蝠算法能够在较短的迭代步数内获取较高的个体适应度,且能够实现较佳的多目标优化结果,符合节能规划的目标需求。(Aiming at the problem that the obje
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
- 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