Abstract: To solve the problem of manufacturing cloud service composition and avoid the optimization results influenced by initial parameters of intelligent algorithms,an approach for manufacturing cloud service composition was proposed based on teaching-learning based optimization.The process of service composition which was based on Quality of Service (QoS) was analyzed.The QoS evaluation model for a single service was proposed,and the overall QoS evaluation method for composite services was analyzed.The mathematical model for manufacturing cloud service composition and optimization problems was established.The teaching-learning based optimization was utilized to solve the optimal problem.Compared with the improved genetic algorithm and the improved particle swarm optimization algorithm,the simulation results showed that the proposed algorithm could solve the cloud service composition problem effectively.

Key words: cloud manufacturing, service composition, teaching-learning-based optimization, quality of service

摘要： 为解决云制造环境下的制造云服务组合优化问题,并排除智能算法的初始值对算法求优结果的影响,提出一种基于教—学算法的制造云服务组合优化算法。分析了基于服务质量的制造云服务组合流程,给出了制造云服务组合的服务质量评估模型和组合服务整体服务质量的评价方法,建立了制造云服务组合问题的数学模型,最后利用教—学算法求解最优组合。将所提方法与改进的遗传算法和改进的粒子群算法进行对比,并通过仿真实验证明了该算法求解制造云服务组合问题的有效性。

关键词: 制造云, 服务组合, 教&mdash, 学算法, 服务质量