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针对云计算环境下云服务体系的构建研究了云服务组合的全局Qo S最优问题,提出一种改进杂交粒子群算法.该算法通过动态因子和二阶振荡机制的引入改进速度更新和动态参数机制,丰富了种群的多样性,并且提高了算法的全局搜索能力.同时采用的杂交策略也降低算法对问题模型的依赖性.通过对无局部Qo S限制和有局部Qo S限制条件下的云服务组合仿真实验结果分析得出该算法能够求得质量更高的全局Qo S值,并且运算耗费时间随问题规模基本呈线性增加趋势,说明该算法能够适用于大规模云服务组合问题.
Aiming at the construction of cloud service system under cloud computing environment, the global Qo S optimal problem of cloud service composition is studied and an improved hybrid particle swarm optimization algorithm is proposed.It improves the speed update and dynamic parameters through the introduction of dynamic factor and second-order oscillation mechanism Mechanism to enrich the diversity of the population and improve the global search ability of the algorithm.Meanwhile, the hybrid strategy also reduces the dependence of the algorithm on the problem model.Through the research on the cloud without the local Qo S restrictions and the local Qo S restrictions The result of service composition simulation results shows that this algorithm can obtain higher quality global Qo S value, and the operation time increases linearly with the scale of problem, indicating that this algorithm can be applied to large-scale cloud service composition problem.