From ‘simulation optimization’ to ‘simulation configuration’ of systems

Simulation optimization aims at determining the best values of input parameters, given an output criterion. We address here the much more complex case where design options have to be chosen, which can themselves necessitate choices about other sub-options, as well as numerical parameters. This so-called configuration problem is addressed using simulation associated with a distributed evolutionary algorithm, and is illustrated through an example. This approach allows a large variety of new types of problems to be solved, such as design of complex dynamic systems. In the light of these results, the limits in the optimized configuration of systems are shown to be now determined by the modeling and simulation tools and concepts. In this respect, a possible research direction, based on so-called functional simulation, is discussed and some research perspectives are introduced.