Self-Adaptive and Self-Organizing (SASO) systems aim to tackle the challenges of information technology’s growing complexity. While various control patterns exist to coordinate such systems, this Ph.D. project takes a fully centralized perspective as its starting point. The focus lies on developing a Central Control Unit (CCU) capable of multi-objective, self-aware optimization while balancing potentially conflicting local decisions. As part of the InHOSaS project, this research, along with a second Ph.D. project, will ultimately integrate the CCU with autonomous decision-making at the subsystem level, forming a hybrid approach. These methods are exemplified using the platooning use case, demonstrating the coordination of semi-automated or automated vehicles within the SASO framework to maintain both individual subsystem goals and overarching system constraints.