moea

Research on the Robustness and Complexity Performance of Solution Set in MOEA

多目标进化算法解集的鲁棒性与复杂性能研究

For the multi-objective multi-factory capacity allocation planning problem , we propose a two-stage model for the problem and develop a SA based MOEA to solve the model .

对于多目标多工厂产能分配计划问题，本文建立了两阶段求解模型，并提出了用基于SA的多目标进化算法进行求解。

σ Selection MOEA Based Mining 3D Clusters in Microarray Data

基于σ选择MOEA的微阵列数据三维聚类挖掘

Fuel-time Multiobjective Optimal Control of Flexible Structures Based on MOEA / D

基于MOEA/D的柔性结构燃料&时间多目标优化控制研究

Repair Strategies for Multiobjective 0 / 1 Knapsack Problem in MOEA

多目标0/1背包问题MOEA求解中的修复策略

Screening of F ⅻ gene mutation by MOEA .

利用MOEA技术筛选是否存在已知常见的FⅫ基因点突变。

Source : Investment Commission , MOEA , ROC .

资料来源：经济部投资审议委员会。

A Normal Distribution Crossover for ε - MOEA

一种基于正态分布交叉的ε-MOEA

The MOEA will appoint a specific agency to carry out mandated matters of standardization .

关于标准事项，由经济部设专责机关办理。

The MOEA utilizes an adaptive evaluation strategy identifying the true Pareto front , and a diversity strategy is introduced to keep the diversity of the population .

该MOEAs采用了一种新的能够识别真实Pareto前沿形状的自适应个体评价策略，并结合了多样性策略以保证演化过程的种群多样性。

MOEA applied to medical health care , is smaller ? Sized , more easily operated , safer and more economical than other sources of oxygen supply .

膜法富氧用于医疗保健比其它氧源体积小，操作简单，安全和经济。

In this paper , we proposed a multi-objective evolutionary algorithm for non-uniformly distributed multi-optimization problems ( MOEA / NUDP ) .

在此，我们提出一种解决非均匀问题的多目标进化算法。

In this algorithm , we employ the MOEA / D optimize the two conflicting functions in evolutionary k-means and avoid setting the weight parameter in advance .

在该算法中，我们使用MOEA/D同时优化演化k-means（？）中两个相互冲突的函数，克服了传统方法中需要提前设定权重参数的缺陷。

The multi-objective optimization problem is solved by ε - MOEA and cross entropy method . In three case studies , the two algorithms are verified and compared with other four existing algorithms .

本文使用ε-MOEA和交叉熵算法对上述多目标问题进行求解，通过三个实例对其性能进行了验证，并与其他方法进行了对比。

In order to demonstrate the performance of the proposed algorithm , it is compared with the MOEA / D-DE and the hybrid-NSGA-II-DE. The result indicates that the proposed algorithm is efficient .

为了验证提出的算法的性能，我们把它与MOEA/D-DE和混合的NSGA-Ⅱ-DE进行了实验比较，结果表明新的算法具有很好的性能。

The cross entropy method converges rapidly and is more suitable for small-scale model . The ε - MOEA is more flexible and can be applied to models of different size , while its parameters must be determined based on specific case .

其中交叉熵算法收敛速度快，较适宜小规模管网，ε-MOEA更为灵活，能适应不同规模的管网，但算法参数需要根据实际情况加以确定。

Multi-Objective Evolutionary Algorithm ( MOEA ) is used to solve multi-objective optimization problems ( MOOPs ) . In order to improve the efficiency of MOEA , a fast method of constructing non-dominated set called Arena 's Principle is suggested .

多目标进化算法是用来解决多目标优化问题的，为了提高多目标算法的效率，提出了一种快速构造非支配集的方法&擂台法则。