To consider the service-matching degree, the composition harmony degree, and the service composition complexity in cloud manufacturing service composition optimization problems, a new composition optimization approach, called cloud-entropy enhanced genetic algorithm (CEGA), is put forward to solve such problems with multi-objectives. The definitions of service-matching degree, composition harmony degree, and cloud-entropy and the corresponding calculation methods are given. A multi-objective optimization mathematical model of cloud manufacturing service composition is built. The manufacturing task of AGV (automated guided vehicle) is taken as an example to verify the proposed CEGA algorithm on the established composition model. The studied result shows that CEGA converges faster than a standard genetic algorithm with shorter time.

To improve the solution efficiency and reliability of multidisciplinary design optimization (MDO), an enhanced MDO approach, called sequenced collaborative optimization (SCO), is proposed. The proposed approach introduces the design structure matrix (DSM) to describe the coupling effects among disciplines and aggregates those mutually coupling disciplines into the strong tie groups among similar ones and the weak tie among heterogeneous ones through clustering algorithms. Further, those in the same group are sequenced by the DSM division algorithm. Moreover, by adding constraints, the groups are made independent, resulting in a tree structure without loops, thus decoupling the original multidisciplinary problem into several independent collaborative optimization modules. In the end, an example is employed to verify the efficiency and reliability of the approach.