济阳坳陷待发现生物气资源量预测新方法

生物气是特殊成因天然气,由于生成机理尚未明确,成因法难以准确预测其资源量.在对生物气成因类型和生成条件分析的基础上划分了评价单元,提出了基于统计学的待发现生物气资源量预测的新方法,即应用截头移位帕雷托分布模型预测各评价单元待发现生物气藏的规模分布,然后构建三角分布函数预测待发现生物气藏数量,最后用Monte Carlo...     

极值超出量服从广义Pareto分布的载荷谱外推方法研究

提出了极值超出量服从广义Pareto分布的载荷谱外推方法。将所提方法运用到汽车用户典型路面载荷谱的外推中,分别拟合了形状参数为零和不为零的广义Pareto分布函数,对比分析了不同形状参数的分布函数对载荷谱外推结果的影响。将外推前后的载荷谱、采集载荷谱分别从幅值域和频率域进行了对比分析,研究结果表明：基于广义Pareto分布函数形状参数为零的载荷谱外推方法可得到偏于保守的外推结果;基于广义Pareto分布函数形状参数不为零的外推方法得到的外推载荷谱幅值比采集载荷谱幅值偏大。     

基于网格化的移动通信网络资源与业务发展评估分析

在当前5G网络大规模建设、4G网络建设大幅缩减的情况下,针对当前主流运营商网络运营维护网格化的现状,为保障4G网络长久运营,需进一步加强4G网络建设精准性及提升4G投资效益.本文结合当前网络资源和业务发展都是网格化管理的现状,针对性地对4G网络网格内网络资源与市场发展匹配情况进行网格化分析,精准评估区分制约网格业务发展...     

偏振云检测中基于多目标优化的角度选择研究

多角度信息在扩展云检测功能、提高检测精度等优势的同时,带来多角度遍历计算的复杂度和数据规模增大的问题。云检测角度信息来自地表的二向性反射分布函数和大气分子散射效应,尽管在局部区域内存在用分析解确定某些观察角的可能性,但由于卫星运动观察几何变化、云几何因子的影响、地物调查的巨大工作量等实际应用的复杂性,POLDER等官方产品仍采用所有角度遍历计算。由于邻近角度间信息的冗余,文章用平均联合信息熵和K-L信息散度作为角度子集选择的特征,提出了Pareto多目标前沿最优解和理想解算法,在POLDER和"高分五号"卫星搭载的多角度偏振探测仪(directional polarimetric camera,DPC)的数据集上进行云检测实验。2角度组合结果与POLDER产品相比,总体精度89.36%,Kappa系数0.7845,DPC检测分类相似度86%,时间复杂度减少约1/7。实验表明所提方法在保持检测效果的同时具有降低计算开销的优点,可为云检测提供一种快速有效、满意精度和自动化运行的新途径。     

一种基于输运理论的多目标演化算法

提出了一种根据输运理论中的粒子输运方程、相空间能量定律和熵增法则构造的一种能够准确、高效地求解多目标优化问题的多目标演化算法(MOPEA).由于该算法使用了粒子系统从非平衡达到平衡的理论来定义求解多目标问题的Rank函数和Niche适应值函数,使得种群中的所有个体都有机会参与演化操作,以达到快速、均匀地求出多目标优化问题的Pareto最优解.数据实验显示,利用该算法求解多目标优化问题不仅能够使算法快速地收敛到全局Pareto前沿,同时由于该算法要求所有的粒子都要参与杂交和变异等演化操作,从而避免问题早熟现象的出现,并通过与传统演化算法的性能指标分析比较说明,使用该算法求解多目标优化问题具有明显的优越性.     

基于强度Pareto进化的注塑机注射性能多目标优化

为实现大型注塑机注射性能的优化设计,构建了注射压力、注射速率和注射功率优化模型,应用多目标进化算法,系统分析了影响注射性能的各方面因素.改进强度Pareto进化算法,引入模糊C均值聚类,加快外部种群的聚类过程.采用约束Pareto支配和浮点数、二进制混合染色体编码策略,一次运行就能求得分布均匀的Pareto最优解集,并使用基于集合理论的方法选择一个最优解.试验分析表明:结合了强度Pareto进化算法与模糊C均值聚类方法的混合算法在提高注射综合性能的同时,能够获得比线性加权法分布性更好的Pareto前沿;且与强度Pareto进化算法相比,显著缩短了运算时间,具有较高的效率与鲁棒性.     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.     

Transient process relaxation time research under the condition of self-similar traffic input in wireless networks

In the article, the results of measuring the traffic in wireless networks are presented. The results provide evidence of the traffic being notably self-similar. The two following models were developed in order to estimate the parameters of a wireless network operating along with the input traffic being self-similar: a model for systems with no restrictions on the quantity of the orders in the system lines and a model for systems with limited buffer volumes. The two types of incoming traffic that each system was simulated for were the Poisson and self-similar traffic.The main objective of the article is to figure out the relaxation time and dependences of the relaxation time and loss probability on different parameters of the simulated systems.     

Sensitivity aware NSGA-II based Pareto front generation for the optimal sizing of analog circuits

This paper deals with multiobjective analog circuit optimization taking into consideration performance sensitivity vis-a-vis parameters' variations. It mainly considers improving computation time of the inloop optimization approaches by including sensitivity considerations in the Pareto front generation process, not as a constraint, but by involving it within the used metaheuristic evolution process. Different approaches are proposed and compared. NSGA-II metaheuristic is considered. The proposed sensitivity aware approaches are showcased via two analog circuits, namely, a second generation CMOS current conveyor and a CMOS voltage follower. We show that the proposed ideas considerably alleviate the long computation time of the process and improve the quality of the generated front, as well.     

Hybrid algorithms for the twin–screw extrusion configuration problem

The twin-screw configuration problem (TSCP) arises in the context of polymer processing, where twin-screw extruders are used to prepare polymer blends, compounds or composites. The goal of the TSCP is to define the configuration of a screw from a given set of screw elements. The TSCP can be seen as a sequencing problem as the order of the screw elements on the screw axis has to be defined. It is also inherently a multi-objective problem since processing has to optimize various conflicting parameters related to the degree of mixing, shear rate, or mechanical energy input among others. In this article, we develop hybrid algorithms to tackle the bi-objective TSCP. The hybrid algorithms combine different local search procedures, including Pareto local search and two phase local search algorithms, with two different population-based algorithms, namely a multi-objective evolutionary algorithm and a multi-objective ant colony optimization algorithm. The experimental evaluation of these approaches shows that the best hybrid designs, combining Pareto local search with a multi-objective ant colony optimization approach, outperform the best algorithms that have been previously proposed for the TSCP.