求解服务选取问题的混合蚁群优化算法

Hybrid Ant Colony Optimization Algorithm for Service Selection Problem

为解决大规模服务选取问题,提出了一种混合蚁群优化(HACO)算法.该算法先采用动态skyline服务查询过程过滤抽象服务类相关的冗余候选服务,以大力缩减空间提高查找效率,然后利用聚类设计动态构造图来引导蚂蚁的搜索方向,从而确定局部服务选取的搜索区域;基于已经确定的局部服务选取的搜索区域,利用启发式策略选取具体的组合服务.采用标准的真实数据集和综合产生的数据集对所提的方法进行试验评估,以及和最近提出的相关组合服务算法进行对比.实验结果在解的质量和处理时间方面效果显著.

To tackle the QoS-based service selection problem,a novel efficient hybrid ant colony optimization algorithm was proposed.In this algorithm,a skyline query process was used to filter the candidates related with each service class,by which the search space could be greatly shrunk and the solving efficiency was improved in the case of not losing good candidates.Then,varying dynamic construct graph was designed to guide the ant search directions based on a clustering process and some promising search areas could be found after the ACO search process.In order to make a further exploitation for these areas,a heuristic strategy was introduced and used to make a deeper local search.The proposed approach was evaluated experimentally by using standard real datasets and synthetically generated datasets,and compared with the recently proposed related service selection algorithms.The experiments indicated very encouraging results in terms of the quality of solution,and the processing time required.