基于改进蚁群算法的服务组合优化

Optimizing Services Composition Based on Improved Ant Colony Algorithm

为进行服务组合优化及适应服务组合优化过程中Web服务的动态性、不稳定性以及多种QoS属性限制等问题,提出一种多信息素动态更新的蚁群算法MPDACO,包括MPDACO局部优化算法和MPDACO全局优化算法,该算法基于建立的服务组合模型,在基本蚁群算法基础上进行研究和改进,可以适应服务组合优化过程中发生的服务无效以及服务中QoS变化等情况.另外,为使算法能较快地收敛于最优解,在实验基础上对蚁群算法策略进行了改进.为验证以上算法的有效性,在一个旅游领域的服务推荐系统中对算法进行了仿真实验,实验结果表明文中提出的算法较基本蚁群算法及一种应用于服务选择的遗传算法有更好的性能.

In order to optimize services composition, adapt the dynamic and instable characteristics of Web services and the limitation of multi-QoS attributes in the process of services composition, this paper puts forward an algorithm named Multi-pheromone and Dynamically Updating Ant Colony Optimization Algorithm （MPDACO）, which includes one global optimizing algorithm and another local optimizing algorithm. The algorithm, which is based on the ACO and composition model that has been built, can fit for such conditions as service invalidation, QoS changing, etc. In addition, the algorithm has improved the ACO strategy on the basis of experiment to make itself be able to converge to optimal solution. In order to verify the feasibility of the above algorithms, this paper makes a simulation experiment on a prototype in tourism, and the results show that the two algorithms are more effective than ACO and the Genetic Algorithm applied to service selection.