System theoretical complexity (stc): on the benefits of system multiobjective optimization and automatic model composition

Structural and multidisciplinary optimization have been extensively applied to system engineering architecture evaluation. This evaluation have been coupled with analytical methods and/or simulation tools. However, no system theoretical complexity (STC) have been proposed as a driver to these Structural and multidisciplinary optimization algorithms. Contrary to theoretical computer science where theoretical complexity is a mature field used to select the most adequate techniques to solve computer science problems no theoretical foundation exist in system engineering to accurately assess system complexity of multidisciplinary systems and in particular multiphysics systems. In this paper, we will examine several complexity metrics, which are based on the concepts of connectivity, distance, transitivity and centrality and we will extract the most appropriate ones. Our metric analysis on different network models prove that these measures alone are unable to understand all the information about complex systems properties and to distinguish networks structurally. In this paper, we will suggest a novel method including spatial constraints and the most appropriate structural metrics that it is able to quantify networks better than the metrics proposed today. This information is therefore added in system models to be used for model composition and architecture evaluation.