基于改进NSGA-II算法的物联网服务组合优化研究  

Research on IoT Service Composition Optimization Based on Improved NSGA-II Algorithm

作　　者：邱林山 程良伦[1,2] 王涛[1] 

机构地区：[1]广东工业大学自动化学院,广东 广州 [2]广东工业大学计算机学院,广东 广州

出　　处：《计算机科学与应用》2023年第10期1824-1836,共13页Computer Science and Application

摘　　要：物联网服务组合是促进物联网发展和实现资源增值的一项关键技术。对于单个的物联网服务,其功能有限,若将若干物联网服务进行组合得到功能更加强大的复合服务,则可以提升复合服务的性能。以往的物联网服务组合模型研究多关注时间、成本、质量等等,较少考虑国家的能源消耗要求和物联网平台的用户体验问题以及物联网平台的安全性。原始NSGA-II算法在种群迭代过程中容易出现提前收敛或局部收敛问题。考虑上述情况,本文提出一种新的评价模型,并改进NSGA-II算法,并用改进后的算法求解该模型。最后通过一个物联网服务组合的算例,表明改进的NSGA-II算法求解的帕累托面更加平滑,算法运行时间减少6.216%,证明了模型的有效性,以及改进算法的先进性。Service composition of Internet of things (IoT) is a key technology to promote the development of IoT and realize value-added resources. For a single IoT service, its function is limited. If several IoT services are composed to obtain a more powerful composite service, the performance of the composite service can be improved. Previous IoT service portfolio model studies pay more attention to the time, cost, quality, etc., less consider the national energy consumption requirements and Internet of things platform user experience problems. The original NSGA-II algorithm is prone to premature convergence or local convergence in the process of population iteration. Considering the above situation, this paper proposes a new evaluation model, and improve the elite strategy of pareto solutions sorting genetic algorithm (NSGA-II), and the improved algorithm to solve the model. Finally, an internet of things service composition example shows that the Pareto surface solved by the improved NSGA-II algorithm is smoother, and the running time of the algorithm is reduced by 6.216%, which proves the effectiveness of the model and the advancement of the improved algorithm.

关 键 词：物联网服务 服务组合 多目标优化 改进的NSGA-II算法 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]