检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
机构地区:[1]重庆大学软件学院,重庆400044
出 处:《计算机系统应用》2012年第6期81-85,共5页Computer Systems & Applications
基 金:国家自然科学基金(71102065)部分资助
摘 要:为了提高Web服务组合流程中服务选择技术的收敛性能,提出了一种基于遗传算法与蚁群算法相融合的多目标优化策略,用于解决基于QoS的Web服务组合问题。本文首先将Web服务组合的全局最优化问题转化为寻求一条QoS最优解的路径问题,并通过改进遗传算法得到蚁群算法中初始路径的信息素分布,再通过改进蚁群算法来求得最优解。仿真实验结果表明,该改进算法能在较少的进化代数下得到最优路径,提高了Web服务组合的快速全局搜索能力。To improve the convergence ability of service selection technology in process of Web service composition, the paper presents a multi-objective optimization strategy based on genetic algorithm and ant colony algorithm to solve global optimization problem in QoS-based Web service composition. In the paper, global optimization problem in Web service composition is presented as a QoS optimal routing problem. And then, an improved genetic algorithm is proposed to get pheromone distribution in initial route of ant colony algorithm. At last, an improved ant colony algorithm is presented to get the optimal solution. Simulation result suggests that the improved algorithms can get the optimal routing in less evolutional generation than typical algorithms, and improve global research ability in Web Service composition.
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.222