Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services sele...Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services selection)to resolve dynamic Web services selection with QoS global optimal path,was proposed.The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints.The operations of the cross and mutation in genetic algorithm were brought into PSOA(particle swarm optimization algorithm),forming an improved algorithm(IPSOA)to solve the QoS global optimal problem.Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms.展开更多
A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MC...A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.展开更多
An efficient QoS routing algorithm was proposed for multiple constrained path selection. Making use of efficient pruning policy, the algorithm reduces greatly the size of search space and the computing time. Although ...An efficient QoS routing algorithm was proposed for multiple constrained path selection. Making use of efficient pruning policy, the algorithm reduces greatly the size of search space and the computing time. Although the proposed algorithm has exponential time complexity in the worst case, it can get the running results quickly in practical application. When the scale of network increases, the algorithm can efficiently control the size of search space by constraint conditions and prior queue. The results of simulation show that successful request ratio ( r ) of efficient algorithm for multi-constrained optimal path (EAMCOP) is better than that of heuristic algorithm for multi-constrained optimal path (H-MCOP), but average computing time ( t ) of EAMCOP is far less than that of H-MCOP. And it can be seen that the computing time of EAMCOP is only one fourth of that of H-MCOP in Advanced Research Projects Agency Network (ARPANet) topology.展开更多
对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模...对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模态分解算法,通过蚁狮算法自动寻优选取合适的分解次数和惩罚因子,计算分解得到的各分量的分布熵,将其中的噪声分量筛选去除,将其余有效分量进行线性重构得到降噪后的零序电流信号;其次,将经过降噪处理后的一维零序电流信号经格拉姆角场转换为二维图像,制备故障选线数据集;然后,引入预训练的ConvNeXt模型,根据该研究数据模型特征,在其已有权重基础上对模型参数进行对应微调,从而提高模型精度并形成最终的选线模型;最后引入绝对平均误差、均方根误差作为评价指标验证所提降噪算法有效性。分别在加入噪声与否的前提下,将所提模型与3种选线模型相比较。实验结果表明该模型的准确率最高、抗噪性方面更好,其中该研究算法准确率达到了99.82%并且在不同噪声条件下都能维持91%以上的准确率,高于其他选线模型,克服了传统故障选线方法准确率低、抗噪性差的问题。展开更多
针对多目标狼群算法存在的搜索不充分、收敛性不足和多样性欠缺的问题,以及缺少对约束进行处理的问题,提出环境选择的双种群约束多目标狼群算法(multi-objective wolf pack algorithm for dual population constraints with environment...针对多目标狼群算法存在的搜索不充分、收敛性不足和多样性欠缺的问题,以及缺少对约束进行处理的问题,提出环境选择的双种群约束多目标狼群算法(multi-objective wolf pack algorithm for dual population constraints with environment selection,DCMOWPA-ES)。引入双种群约束处理方法给种群设置不同的搜索偏好,主种群运用可行性准则优先保留可行解,次种群通过ε约束探索不可行区域并将搜索结果传递给主种群,让算法能较好应对复杂的不可行区域,保障算法的可行性;提出维度选择的随机游走策略,使人工狼可自主选择游走方向,提高种群的全局搜索能力;设计精英学习的步长调整机制,人工狼通过向头狼学习的方式提升种群的局部搜索能力,确保算法的收敛性;采用环境选择的狼群更新策略,根据人工狼被支配的情况和所处位置的密度信息对其赋值,选择被支配数少且密度信息小的人工狼作为优秀个体,改善算法的多样性。为验证算法性能,将DCMOWPA-ES与六种新兴约束多目标优化算法在两组约束多目标测试集和汽车侧面碰撞设计问题上进行对比实验。实验结果表明,DCMOWPA-ES算法具备较好的可行性、收敛性和多样性。展开更多
基金Project(70631004)supported by the Key Project of the National Natural Science Foundation of ChinaProject(20080440988)supported by the Postdoctoral Science Foundation of China+1 种基金Project(09JJ4030)supported by the Natural Science Foundation of Hunan Province,ChinaProject supported by the Postdoctoral Science Foundation of Central South University,China
文摘Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services selection)to resolve dynamic Web services selection with QoS global optimal path,was proposed.The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints.The operations of the cross and mutation in genetic algorithm were brought into PSOA(particle swarm optimization algorithm),forming an improved algorithm(IPSOA)to solve the QoS global optimal problem.Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms.
基金the Natural Science Foundation of Anhui Province of China (050420212)the Excellent Youth Science and Technology Foundation of Anhui Province of China (04042069).
文摘A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.
文摘An efficient QoS routing algorithm was proposed for multiple constrained path selection. Making use of efficient pruning policy, the algorithm reduces greatly the size of search space and the computing time. Although the proposed algorithm has exponential time complexity in the worst case, it can get the running results quickly in practical application. When the scale of network increases, the algorithm can efficiently control the size of search space by constraint conditions and prior queue. The results of simulation show that successful request ratio ( r ) of efficient algorithm for multi-constrained optimal path (EAMCOP) is better than that of heuristic algorithm for multi-constrained optimal path (H-MCOP), but average computing time ( t ) of EAMCOP is far less than that of H-MCOP. And it can be seen that the computing time of EAMCOP is only one fourth of that of H-MCOP in Advanced Research Projects Agency Network (ARPANet) topology.
文摘对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模态分解算法,通过蚁狮算法自动寻优选取合适的分解次数和惩罚因子,计算分解得到的各分量的分布熵,将其中的噪声分量筛选去除,将其余有效分量进行线性重构得到降噪后的零序电流信号;其次,将经过降噪处理后的一维零序电流信号经格拉姆角场转换为二维图像,制备故障选线数据集;然后,引入预训练的ConvNeXt模型,根据该研究数据模型特征,在其已有权重基础上对模型参数进行对应微调,从而提高模型精度并形成最终的选线模型;最后引入绝对平均误差、均方根误差作为评价指标验证所提降噪算法有效性。分别在加入噪声与否的前提下,将所提模型与3种选线模型相比较。实验结果表明该模型的准确率最高、抗噪性方面更好,其中该研究算法准确率达到了99.82%并且在不同噪声条件下都能维持91%以上的准确率,高于其他选线模型,克服了传统故障选线方法准确率低、抗噪性差的问题。
文摘针对多目标狼群算法存在的搜索不充分、收敛性不足和多样性欠缺的问题,以及缺少对约束进行处理的问题,提出环境选择的双种群约束多目标狼群算法(multi-objective wolf pack algorithm for dual population constraints with environment selection,DCMOWPA-ES)。引入双种群约束处理方法给种群设置不同的搜索偏好,主种群运用可行性准则优先保留可行解,次种群通过ε约束探索不可行区域并将搜索结果传递给主种群,让算法能较好应对复杂的不可行区域,保障算法的可行性;提出维度选择的随机游走策略,使人工狼可自主选择游走方向,提高种群的全局搜索能力;设计精英学习的步长调整机制,人工狼通过向头狼学习的方式提升种群的局部搜索能力,确保算法的收敛性;采用环境选择的狼群更新策略,根据人工狼被支配的情况和所处位置的密度信息对其赋值,选择被支配数少且密度信息小的人工狼作为优秀个体,改善算法的多样性。为验证算法性能,将DCMOWPA-ES与六种新兴约束多目标优化算法在两组约束多目标测试集和汽车侧面碰撞设计问题上进行对比实验。实验结果表明,DCMOWPA-ES算法具备较好的可行性、收敛性和多样性。
