改进蚁群算法在基于服务质量的Web服务组合优化中的应用  

Improved ant colony optimization for Qo S-based Web service composition optimization

作　　者：倪志伟[1,2] 方清华[1,2] 李蓉蓉[1,2] 李一鸣[1,2] 

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]过程优化与智能决策教育部重点实验室(合肥工业大学),合肥230009

出　　处：《计算机应用》2015年第8期2238-2243,2279,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(71271071;71490725);国家自然科学基金青年项目(71301041);国家863计划项目(2011AA040501)

摘　　要：为了克服基础蚁群算法存在的前期搜索速度较慢、后期极易陷入局部最优解的缺点,提出初始信息素分布策略和局部优化策略;同时还提出了依赖解的质量的信息素更新依据,以增强算法过程中信息素的有效积累。将该改进蚁群算法应用于基于服务质量(Qo S)的Web服务组合优化问题中,通过在数据集QWS2.0上的实验对改进蚁群算法的可用性和有效性进行了验证。结果表明改进的蚁群算法与基础蚁群算法、利用解与理想解距离更新信息素的改进蚁群算法以及用支配程度作为解的个体评价的改进遗传算法相比,能够找到更多的非劣解,寻优能力更优,表现出了较稳定的性能。The basic Ant Colony Optimization （ACO） has slow searching speed at prior period and being easy to fall into local optimum at later period. To overcome these shortcomings, the initial pheromone distribution strategy and local optimization strategy were proposed, and a new pheromone updating rule was put forward to strengthen the effective accumulation of pheromone. The improved ACO was used in QoS-based Web service composition optimization problem, and the feasibility and effectiveness of it was verified on QWS2.0 dataset. The experimental results show that, compared with the basic ACO, the improved ACO which updates the pheromone with the distance of the solution and the ideal solution, and the improved genetic algorithm which introduces individual domination strength into the environment selection, the proposed ACO can find more Pareto solutions, and has stronger optimizing capacity and stable performance.

关 键 词：WEB服务 服务组合技术 蚁群算法 PARETO最优解 局部优化 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]