制造云服务组合是一种提高云制造资源利用率,实现制造资源增值的新技术,对云制造产业的快速发展具有重要的支撑作用.随着云制造技术的日益成熟,网络上出现了大量具有相同制造功能和不同服务质量的制造云服务,如何通过这些制造云服务构建出既能满足用户制造需求,又具有最优服务质量的组合服务是云制造领域面临的难题.针对这一问题,将协作学习、变异和精英保留机制引入最大最小蚁群算法,构造了具有学习和变异能力的最大最小蚁群算法,并使用该算法求解服务质量感知的制造云服务优化组合问题.仿真实验结果验证了算法的有效性.%Manufacturing cloud service composition is a new technology to improve the utilization and achieve the appreciation of cloud manufacturing resources, and support the rapid development of cloud manufacturing industries. As the cloud manufacturing becomes more sophisticated, a large number of manufacturing cloud services with the same founctionalities and different quality of service appear. How to build a composite service through these manufacturing cloud services that not only can meet user' s quality demand, but also has optimal quality is a challenging problem. To solve this problem, this paper introduces the Collaborative Learning (CL) and elite retation mechanism to the Max-Min Ant System (MMAS), constructs a new optimal algorithm with learning ability, and then applies this algorithm to solve the problem of otpimal manufacturing cloud service composition. The Simulation results validate the effectiveness of this algorithm.
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