Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing

Developments in new information technology have indicated that single manufacturing services are now unable to satisfy users' multi-objective demands, especially in the process industry. As a new user-centric, service-oriented, demand-driven manufacturing model, cloud manufacturing can provide high-reliability, low-cost, fast-time, high-ability services. This study presents a new Manufacturers to Users (M2U) mode for cloud manufacturing, aiming at solving the core manufacturing service composition optimal selection (MSCOS) problem. The M2U mode expands the service areas and improves its dynamic optimal allocation capabilities of resources by efficient and flexible management and operation of services. Firstly, a comprehensive mathematical evaluation model with four critical quality of service (QoS)-aware indexes (time, reliability, cost, and ability) is constructed. Secondly, a new information entropy immune genetic algorithm (IEIGA) is proposed for the model solution. Finally, nine MSCOS problems of different scales are illustrated so as to compare the performance of the three algorithms. The results prove the effectiveness and superiority of the proposed algorithm and its suitability for solving large-scale service composition problems.