Hierarchical Multiobjective Optimization - Ringkamp, Maik; Dellnitz, Michael (Develop Intelligent Technical Systems of the Future, 2014)
After the domain-spanning conceptual design, engineers from different domains work in parallel and apply their domain-specific methods and modeling languages to design the system. Vital for the successful design, are system optimization methods and the design of the reconfiguration behavior. The former methods enable the parametric adaption of the system’s behavior, e.g. an adaption of controller parameters, according to a current selection of the system’s objectives. The latter realizes structural adaption of the system’s behavior, e.g. the exchange of software or hardware parts. Altogether, this leads to a complex system behavior that is hard to overview. In addition, self-optimizing systems are used in safety-critical environments. Consequently, the system’s safety-critical behavior has to undergo a rigorous verification and testing process. Existing design methods do not address all of these challenges together. Indeed, a combination of established design methods for traditional technical systems with novel methods that focus on these challenges is necessary. In this chapter, we will focus on such new methods. We will introduce new system optimization and design methods to develop reconfigurations of the software and the microelectronics. In order to ensure the correctness of safety-critical functionality, we propose new testing methods and formal methods to ensure safety-properties of the software. We show how to apply virtual prototyping to deal with the complexity of self-optimizing systems and perform an early analysis of the overall system. As each domain applies its own modeling languages, the result of these methods are several overlapping models. In order to keep these domain-specific models consistent among all domains, we will introduce a new semi-automatic model synchronization technique. Each of these design methods are integrated with the reference process for the development of self-optimizing system