改进蚁群算法及其在云服务组合优化中的应用研究

摘要

Aiming at the dynamic, instability, multiple QoS attribute restrictions and other issues in service composition process, we propose an optimized and service combination fitted dynamic aggregation pheromone updating ant colony algorithm (WJ-I-ACO) , including improved local optimization algorithm based on clustering analysis and improved global optimization algorithm based on dynamic differential.The effectiveness and feasibility of the algorithm are verified through MATLAB simulations.Based on this, we analyze the optimization strategy of cloud service composition and give the path optimization method for service composition.%针对服务组合过程中的动态性、不稳定性以及多种QoS属性限制等问题,提出一个适应服务组合的改进蚁群算法WJ-I-ACO算法,包括基于聚类分析方法的改进局部优化算法和基于动态差分的改进全局优化算法.通过MATLAB仿真实验设计,验证了算法的有效性和可行性;基于此,分析了云服务组合的优化策略,给出了服务组合的路径寻优方法.