Two methods are available to you for the optimization process, allowing you to find optimal parameter values based on a weight or deformation criterion.
The most efficient method with the shortest calculation time is nature-inspired particle swarm optimization (PSO). Have you heard or read about it? This artificial intelligence technology (AI) has a strong analogy to the behavior of animal swarms searching for a resting place. In such swarms, you find numerous individuals (cf. optimization solution - e.g., weight) that like to stay in a group and follow the group's movement. Let's assume that each swarm member has the need to rest at an optimal resting place (cf. best solution - e.g., lowest weight). This need increases as they approach the resting place. Thus, swarm behavior is also influenced by the properties of the space (cf. result diagram).
Why the excursion into biology? Quite simply, the PSO process in RFEM or RSTAB proceeds similarly. The calculation run begins with an optimization result from a random allocation of the parameters to be optimized. It continuously determines new optimization results with varied parameter values based on the experience of previously made model mutations. This process continues until the specified number of possible model mutations is reached.
Alternatively to this method, a batch processing method is also available in the program. This method attempts to check all possible model mutations by randomly assigning values to the optimization parameters until the specified number of possible model mutations is reached.
Both variants also check the activated design results of the add-ons after calculating a model mutation. Furthermore, they save the variant where the utilization is < 1 along with the associated optimization result and parameter value allocation.
