moga

The performance of an MOGA can be measured from three aspects : the convergence to the true Pareto optimal front , the diversity of solutions and the time consuming .

一个多目标遗传算法的优劣主要看三个指标：解集收敛程度，解集分布度以及时间消耗。

Application of MOGA in Conflict Resolution for Architecture Cooperative Design

多目标优化遗传算法在建筑协同设计冲突消解中的应用

Modeling of Fermentation Process Based on MOGA and SVM

基于MOGA和SVM的发酵过程建模

Reservoir Characterization and Reservoir Engineering Study of Fula Field and Moga Field in Sudan

苏丹Fula油田和Moga油田油藏特征和油藏工程研究

The effectiveness of MOGA is validated , and it can select parameters automatically .

MOGA方法的有效性也得到了验证，它也能够自动选择最优参数。

Multi-objective genetic algorithm ( MOGA ) is used to determine the optimal parameters of the microbial fermentation process .

针对微生物发酵过程，使用多目标遗传算法（multi-ObjectiveGeneticAlgorithm，MOGA）确定最优参数。

Multi-Objective Genetic Algorithm based on Pareto ( MOGA ) is one of the most important algorithms to solve Multi-Objective problems .

基于Pareto最优概念的多目标遗传算法是处理多目标优化问题的一个重要算法。

MOGA ( multi-objective genetic algorithm ) is a method developed from genetic algorithm , it can directly weigh the balance among multiple objectives .

多目标遗传算法（MOGA）是在遗传算法的基础上发展起来的，它可以进行多个目标之间直接权衡。

Among many methods that how to dealing with multi-objective optimization problems by using the multi-objective genetic algorithm ( MOGA ) has played an important role .

在处理多目标优化问题的众多方法中，多目标遗传算法发挥了重要的作用。

The success of the proposed satellite wells indicates the reserve potential area is in between Moga and Fula oil fields .

滚动勘探开发的成功表明Fula油田和Moga油田之间是原油储量增长的潜力区。

Then , Multi-objective Genetic Algorithm ( MOGA ) was used to search for the optimal parameters of the module and step width of motion .

在第三章中首先引入了移动机器人规划环境的矢量场模型，然后利用变权重多目标遗传算法对规划参数进行优化。

The Multi-objectives Genetic Algorithms ( MOGA ) as representative evolution algorithms was considered specially suit to solve this kind of questions .

以多目标遗传算法为代表的进化算法被认为特别适合求解此类问题。

An optimal design model for physical structure of RITS and a set of solutions based on multi-objective GA ( MOGA ) are presented in the paper .

本文提出了RITS物理结构优化设计问题模型和基于多目标优化的解决思路。

Most multi-objective genetic algorithms ( MOGA ) use Pareto domination to guide the search , so constructing the non-dominated set became important work .

在多目标遗传算法中，使用Pareto支配关系来指导算法搜索成为当前多目标遗传算法的发展的趋势。

Since a group of Pareto trade-off solutions can be obtained by multi objective genetic algorithm ( MOGA ) in a single run , many researchers become interested in MOGA .

由于多目标遗传算法能够通过一次运行找到一组多目标优化问题的Pareto折衷解，所以受到了国内外众多研究者的广泛关注。

A new modeling method that combines MOGA with support vector machine ( SVM ) regression is presented , with which the penicillin titer pre-estimation model is developed with data collected from real plant .

MOGA和SVM回归相结合形成一种新的建模方法，该方法利用现场生产数据建立了青霉素效价预估模型。

Based on Finite Markov Chain theory , a new convergence analysis framework for multi-objective optimization genetic algorithm ( MOGA ), which aims to converge to Pareto optimal set rather than single optimal point is presented .

寻找非劣解集合是遗传算法求解多目标优化问题的目标，而标准的遗传算法收敛性分析方法对多目标遗传算法的分析并不合适。

In this paper , single objective genetic algorithm ( SOGA ) and multiobjective genetic algorithm ( MOGA ) are developed to optimize the architecture of radial basis function neural network ( RBFNN ) .

本文研究了两种遗传算法设计RBF神经网络的结构的方法：单目标遗传算法与多目标遗传算法。

In real application , optimization for single target usually cannot achieve requirement , for which multi-target GA concept is supposed , coefficient weighting method , VEGA and MOGA based on Pareto order are introduced , and optimization procedure is also given .

在实际应用中单一目标的优化往往不能满足要求，进而引入了多目标遗传算法的概念，对系数加权法、VEGA、基于Pareto秩MOGA方法进行了介绍，给出了优化流程。

Then select three indicators including Product , Corporate Governance , Community Relations and together with the traditional Variance and Expected Returns which constitute the five objectives portfolio . Further this study use multi-objective genetic algorithm ( MOGA ) to solve the model .

选取其中的社区关系、产品和公司治理三个指标与传统的协方差和预期收益构成五个目标函数的多目标投资组合，进一步采用多目标遗传算法（MOGA）对该组合进行求解。

Based on the Pareto optimal conception , a kind of MOGA ( multi-objective genetic algorithms ) seeking non-inferior solution set of MO problems was proposed , while the filtering of non-inferior solutions and the measure of fitness degree were discussed as an emphasis .

基于多目标优化问题Pareto最优解的概念，提出了一种求解非劣解集的多目标遗传算法，重点讨论了算法实现中非劣解集的筛选和适应度的计算。

Based on the research of many scholars , we have analysed the adaptive genetic algorithms that had been proposed , and have proposed two new adaptive genetic algorithms , which are used to solve the 0-1 knapsack problem.1.The mutation only genetic algorithm ( MOGA ) have been proposed .

本文在众多学者研究的基础上，对已有自适应遗传算法进行深入剖析，进而提出两个新的自适应遗传算法，并且用于解决0-1背包问题。