Model Optimization for RSTAB 9

• Artificial intelligence technology (AI): Particle swarm optimization (PSO)
• Structure optimization according to the minimum weight or deformation
• Use of any number of optimization parameters
• Specification of variable ranges
• Optimization of cross-sections and materials
• Parameter definition types
• Optimization | Ascending or Optimization | Descending
• Application of parametric models and blocks
• Code-based JavaScript parametrization of blocks
• Optimization taking into account the design results
• Tabular display of the best model mutations
• Real-time display of the model mutations in the optimization process

There are two methods that you can use for the optimization process, with which you can find optimal parameter values according to a weight or deformation criterion.

The most efficient method with the littlest calculation time is the near-natural particle swarm optimization (PSO). Have you heard or read about it? This artificial intelligence (AI) technology has a strong analogy to the behavior of flocks of animals, looking for a resting place. In such swarms, you can find many individuals (cf. optimization solution - for example, weight) who like to stay in a group and follow the group movement. Let's assume that each individual swarm member has a need to rest at an optimal resting place (cf. best solution - for example, lowest weight). This need increases as the resting place is approached. Thus, the 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 in a similar way. The calculation run starts with an optimization result from a random assignment of the parameters to be optimized. It repeatedly determines new optimization results with varied parameter values, which are based on the experience of the previously performed model mutations. The process continues until the specified number of possible model mutations is reached.

As an alternative to this method, the program also offers you a batch processing method. This method attempts to check all possible model mutations by randomly specifying the values for the optimization parameters until a predetermined number of possible model mutations is reached.

After calculating a model mutation, both variants also check the respective activated design results of the add-ons. Furthermore, they save the variant with the corresponding optimization result and value assignment of the optimization parameters if the utilization is < 1.