Cloud Task Decomposition Based on Improved NSGA-II

This paper investigates task decomposition in the cloud manufacturing environment. Firstly, in this study, the multi-objective task decomposition method with the optimization goal of the maximum internal correlation of subtasks, the minimum external coupling between subtasks, and the mini-mum subtask workload balance is studied. Secondly, the non-dominated sorting genetic-II algorithm (NSGA-II) is improved from three aspects: initialization, parent selection, and mutation mode. Then, the improved NSGA-II (iNSGA-II) is used to solve the problem. Finally, the experiment is carried out to verify the effectiveness of the model and algorithm.