Network functions virtualization(NFV) increases network flexibility and scalability by virtualizing network functions running on the general servers and opens the network innovations by outsourcing VNF instances in 5G...Network functions virtualization(NFV) increases network flexibility and scalability by virtualizing network functions running on the general servers and opens the network innovations by outsourcing VNF instances in 5G networks.However,it leads to the incompatibility issue among different VNF instances,which makes operators difficult to determine which VNF instances to select for Service Function Chains(SFCs).In this paper,we divide VNF instances with high compatibility into clusters used for combining VNF instances in 5G networks.Firstly,we define compatibility among different VNF instances.Secondly,aiming to maximize compatibility of each cluster,we propose a novel hypergraph clustering model that divides the VNF instances into multiple clusters.Then,the hypergraph clustering model is transformed to an evolutionary game.Thus,the cluster establishing is transformed to the game equilibrium searching.Furthermore,we propose a discrete time high order replicator dynamic algorithm to find the game equilibrium.Finally,the simulation results show that the proposed approach can improve the quality of SFCs.展开更多
针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSS...针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSSMPIO)。首先利用基于聚类的特殊拥挤距离非支配排序方法(clustering-based special crowding distance,CSCD)初始化种群;引入自适应物种形成策略生成稳定的小生境,在不同的小生境子空间并行搜索和保持等价Pareto最优解;采用特殊拥挤距离非支配排序策略实现个体选优、精英学习策略避免过早收敛。通过在14个多模态多目标函数上进行测试,并与7种新提出的多模态多目标优化算法进行对比实验以及Wilcoxon秩和检验发现,CSSMPIO的总体性能优于对比算法。最后将算法用于基于地图的测试问题,进一步证明了算法的有效性。展开更多
文摘为了提高辨识稳定图中真实模态的准确性与自动化程度,首先,从稳定点定义方式的角度论述了聚类算法效果欠佳的原因,并采用异阶系统非等权重的定义方式输出稳定点;其次,基于数据挖掘思想,采用改进的辨识聚类结构的有序点(ordering points to identify the clustering structure,简称OPTICS)算法自动清洗稳定点集,通过遍历性搜索的方式确定输入参数;然后,提出结合度矩阵去噪的自适应局部密度谱聚类(local density adaptive spectral clustering,简称SC-DA)算法分析稳定点集,并以簇中值作为模态参数的代表值,实现模态参数的自动化识别;最后,将含有密集模态的外滩大桥作为识别对象进行试验验证。试验结果表明:所提出方法具有较高的精度,与频域分解(frequency domain decomposition,简称FDD)法的频率结果最大相差仅为0.012 3 Hz,且在线识别的准确率达到82.86%,显著高于基于层次聚类的自动识别方法,实现了无人工干预下模态参数的自动、准确识别,具有一定的工程应用前景。
基金supported by The National High Technology Research and Development Program of China(863)(Grant No.2014AA01A701,2015AA01A706)
文摘Network functions virtualization(NFV) increases network flexibility and scalability by virtualizing network functions running on the general servers and opens the network innovations by outsourcing VNF instances in 5G networks.However,it leads to the incompatibility issue among different VNF instances,which makes operators difficult to determine which VNF instances to select for Service Function Chains(SFCs).In this paper,we divide VNF instances with high compatibility into clusters used for combining VNF instances in 5G networks.Firstly,we define compatibility among different VNF instances.Secondly,aiming to maximize compatibility of each cluster,we propose a novel hypergraph clustering model that divides the VNF instances into multiple clusters.Then,the hypergraph clustering model is transformed to an evolutionary game.Thus,the cluster establishing is transformed to the game equilibrium searching.Furthermore,we propose a discrete time high order replicator dynamic algorithm to find the game equilibrium.Finally,the simulation results show that the proposed approach can improve the quality of SFCs.
文摘针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSSMPIO)。首先利用基于聚类的特殊拥挤距离非支配排序方法(clustering-based special crowding distance,CSCD)初始化种群;引入自适应物种形成策略生成稳定的小生境,在不同的小生境子空间并行搜索和保持等价Pareto最优解;采用特殊拥挤距离非支配排序策略实现个体选优、精英学习策略避免过早收敛。通过在14个多模态多目标函数上进行测试,并与7种新提出的多模态多目标优化算法进行对比实验以及Wilcoxon秩和检验发现,CSSMPIO的总体性能优于对比算法。最后将算法用于基于地图的测试问题,进一步证明了算法的有效性。
