基于改进蚁群算法的Web服务组合优化

Web service combination optimization based on improved ant colony algorithm

为解决基础蚁群算法存在的前期搜索速度慢、后期容易陷入局部最优解的问题,针对服务组合的动态性、不稳定性以及非功能属性限制等情况,提出基于改进蚁群算法的Web服务组合优化方法。首先分别介绍基本蚁群算法和L-I-ACO改进蚁群算法,再将其应用到Web服务组合优化建模中,最后通过对比实验测试两种算法的性能。实验结果表明,L-I-ACO改进蚁群算法性能较好,它弥补了基础蚁群算法的不足,提高了动态组合优化过程中的准确率和效率,更利于选取符合客户要求的服务。

In order to solve the problem that the basic ant colony optimization algorithms has a slow search speed in the early stage and is easy to fall into the local optimal solution in the later stage,a Web service combinatorial optimization method based on improved ant colony optimization algorithms is proposed for the dynamic,unstable and non functional attribute constraints of service composition.First,the basic ant colony optimization algorithms and L-I-ACO improved ant colony optimization algorithms are introduced respectively,and then they are applied to the modeling of combinatorial optimization of Web services.Finally,the performance of the two algorithms is tested through comparative experiments.The experimental results show that the L-I-ACO improved ant colony optimization algorithms has good performance,which makes up for the shortcomings of the basic ant colony optimization algorithms,improves the accuracy and efficiency in the dynamic combinatorial optimization process,and is more conducive to selecting services that meet customer requirements.