On the use of analytical target cascading and augmented lagrangian coordination for multidisciplinary design optimization

Analytical target cascading (ATC) and augmented Lagrangian coordination (ALC) are two newly developed multidisciplinary design optimization (MDO) methods. This paper summarizes and analyzes their differences from the aspects of the literature review, basic principles, as well as the optimization models and applications. Their respective solution frameworks for complex systems are proposed. Especially, the relationships and inheritance of the two methods are analyzed from the mathematical models for the first time. Both the computational accuracy and efficiency of the two methods are compared, showing that ALC outperforms ATC for both criteria while owning higher flexibilities in system decomposition. Yet those decomposed into hierarchical structures can be simply solved by ATC to prevent the selection of decomposition parameters which impacts the computational efficiency. This paper provides a comprehensive reference for scholars and enables the scientific selection and application of ATC and ALC to solve MDO problems as complex mechanical design.