nsga

Stand-alone hybrid wind / pv power systems using the nsga - ⅱ

基于NSGA-Ⅱ的风光互补独立供电系统多目标优化

Sintering Burdening Optimization Taking Properties into Account Based on NSGA - ⅱ

基于NSGA-Ⅱ考虑性能的烧结矿配料优化

Research of grid workflow scheduling based on combination of DE and NSGA - ⅱ algorithm

融合DE和NSGA-Ⅱ算法的网格工作流调度研究

Application of Improved NSGA - ⅱ in Wireless MD Video

改进NSGA-Ⅱ在无线MD视频中的应用

Approach to improve bi-objective optimization efficiency of NSGA - ⅱ

快速提高NSGA-Ⅱ算法双目标优化效率的方法

Optimization of Regional Coverage Reconnaissance Satellite Constellations by NSGA - ⅱ Algorithm

基于NSGA-Ⅱ算法的区域覆盖卫星星座优化

Finally , an example strongly illustrated the effectiveness of the NSGA - ⅱ .

最后，通过一个算例，有力地证明了NSGA-Ⅱ算法的有效性。

Application of NSGA ⅱ in Emergency Material Storage Layout Optimization Delivery Consolidation and Stock Replenishment in VMI System

NSGAⅡ在应急物资储备库选址中的应用供应商管理库存系统中库存和运输计划整合

Realizing the robust optimization of structures by combining the NSGA - ⅱ algorithm with the Monte Carlo simulation Technology

结合NSGA-Ⅱ算法和蒙特卡罗模拟技术实现结构的鲁棒优化

We verified the improved NSGA - ⅱ by experiment , it can improve convergence speed in some extent .

通过实验对改进后的NSGA-Ⅱ进行了验证，一定程度上提高了算法的求解速度。

Remote sensing satellite orbit and constellation design method based on multi-objective optimization algorithm ( improved NSGA - ⅱ algorithm ) was proposed .

提出了基于多目标优化算法（改进的NSGA&Ⅱ算法）的遥感卫星轨道和星座设计方法。

And Non-dominated Sorting Genetic Algorithm with elitist strategy ( NSGA - ⅱ) was proposed to solve the multi-objective optimization problem .

建立了横梁和伺服控制系统多目标优化模型，并提出了应用带精英策略的非支配排序遗传算法NSGA-Ⅱ进行多目标优化求解。

This paper uses the improvement genetic algorithm NSGA - ⅱ to solve this question , obtains a series of most superior scheme of arrangement .

采用改进的遗传算法NSGA-Ⅱ来求解这个问题，得到一系列最优布置方案。

No-dominated Sorting Genetic Algorithm ( NSGA - ⅱ) is a multi-objective optimization algorithm , it used to solve the multi-objective optimization problem .

在负载自适应数据库中，对负载进行控制过滤是一个典型的多目标优化问题，非支配排序遗传算法NSGA-Ⅱ是一种多目标优化搜索算法，专门用来解决多目标优化问题。

Among them , Non-dominated Sorting Genetic Algorithm ( NSGA ) and its improved method ( NSGA-II ) have been developed quickly and applied widely .

本文研究的非支配排序遗传算法（Non-dominatedSortingGeneticAlgorithm，NSGA）及其改进算法NSGA-Ⅱ就是其中发展较快、优化效果较好的一种方法。

In order to verify the effectiveness of the algorithm , this paper compares the improved Particle Swarm Optimization Algorithm with NSGA - ⅱ in the same experimental environment .

为了验证算法的有效性，在相同的实验环境中，将本文设计的改进的粒子群优化算法与NSGA-Ⅱ算法进行比较。

We will amend the individuals by introduced greedy algorithm into NSGA - ⅱ, which can improve convergence speed of NSGA - ⅱ algorithm .

将贪心算法引入到NSGA-Ⅱ算法中，对种群中的个体进行修正、优化来提高算法的求解速度。

With the introduction of multi-objective optimization theory , performance evaluation based on Pareto rules for scheduling algorithms is given and the NSGA - ⅱ based link scheduling algorithm is proposed .

引入多目标优化理论，给出算法性能评价的Pareto标准，提出一种基于NSGA-II的链路调度机制。

In this paper Non-dominated Sorting Genetic Algorithms-II , namely NSGA-II , is adopted which is the improved algorithm for NSGA .

在本论文中采用非劣排序遗传算法的改进算法精英保留的非劣排序遗传算法NSGA-II。

The paper analyzed the multi-objective optimization question , and proposed a kind of improvement genetic algorithm which is NSGA - ⅱ, to solve the yacht cabin arrangement optimization question .

分析了多目标优化问题基本概念，提出了一种改进的遗传算法NSGA-Ⅱ，并用此算法求解游艇舱室布置优化问题。

Nondominated Sorting Genetic Algorithm ( NSGA ) shows great advantages in the problems of multi-objective optimization , but it has also aroused criticisms in several aspects after widely used .

第一代非支配排序遗传算法NSGA在多目标领域中显示出比较大的优势，但是随着应用范围的不断拓宽，其缺点就不断暴露出来。

Multi-objective optimization of the bearings based on approximation models was conducted using Non-dominated Genetic Algorithm ( NSGA ) . Oil Consumption and friction loss of the bearings were reduced .

采用非支配遗传算法（NSGA）对轴承进行基于近似模型的多目标优化，降低了轴承的机油流量和摩擦功耗。

Based on the NSGA - ⅱ algorithm studied and analyzed , we improved its crowding mechanism by introducing the Niche theory to expedite its convergence velocity and improve its convergence precision .

为加快收敛速度，提高收敛精度，在已有算法（NSGA-Ⅱ）的基础上，引进小生境思想，提出了更为合理的排挤机制。

In this paper , multi-objective genetic algorithm NSGA - ⅱ was used to solve the multi-objective optimization model , and obtain Pareto solutions for the designers choose to improve the design flexibility .

本文应用多目标遗传算法NSGA-Ⅱ，得到优化模型的多目标不同权重下的Pareto解，供设计人员自行选择，提高了设计的灵活性。

Then the mul-ti-objective optimal signal timing model have been established . Thirdly , we have focused on studying NSGA - ⅱ algorithm which is often used in solving multi-objective models .

再次，重点研究了NSGA-II算法在多目标优化模型求解中的应用。

In order to solve the problems of multi-objective optimization more effectively , the Elitist Nondominated Sorting Genetic Algorithm ( NSGA-II ) is proposed on the basis of NSGA .

为了更好地解决多目标优化问题，在NSGA的基础上，研究人员提出了带精英策略的非支配排序遗传算法NSGA-Ⅱ。

Pareto-MEC and SP-MEC are respectively compared with the reference algorithms of Rand , VEGA , NSGA and SPEA .

分别将Pareto一MEC和SP一MEC与Rand，VEGA，NSGA和SPEA这四种算法进行了比较实验。

The basic principle and method of genetic algorithm and an updating genetic algorithm-elitist non-dominated sorting genetic algorithm & elitist non-dominated sorting genetic algorithm ( NSGA - ⅱ) are introduced .

本文阐述了遗传算法的基本原理和方法，并着重介绍了一种改进的遗传算法&精英保留非劣排序遗传算法（NSGA-Ⅱ），并将其应用于化工中的多目标优化。

On account of the shortcomings and limitations of the traditional strategies for solving multi-objective optimization problems , this thesis studies a multi-objective optimization evolutionary algorithms genetic algorithm , and its improved model NSGA - ⅱ .

鉴于传统多目标优化求解策略的不足和局限，本文进一步研究了一种进化多目标优化算法遗传算法，以及其改进型NSGA-Ⅱ算法。

Translating different responses of long span bridge into multi-objects to optimize at the same time by using NSGA - ⅱ optimization algorithm . It avoids the subjective influence of choosing weight coefficient by human .

采用NSGA-Ⅱ作为优化算法，将大型桥梁取得的各种响应信息转化为多目标同时进行优化，避免了人为选取权重系数给模型修正带来的主观性影响。