SteerFit uses the SteerSuite framework and additional plugins to optimize parameter settings for dynamic navigation algorithms. This work also includes multi-objective optimization of competative metrics to give an artist/crowd author more control over the behaviour of a crowd.
In the context of crowd simulation, there is a diverse set of algorithms that model steering. The performance of steering approaches, both in terms of quality of results and computational efficiency, depends on internal parameters that are manually tuned to satisfy application-specific requirements. This paper investigates the effect that these parameters have on an algorithm's performance. Using three representative steering algorithms and a set of established performance criteria, we perform a number of large scale optimization experiments that optimize an algorithm's parameters for a range of objectives.
For example, our method automatically finds optimal parameters to minimize turbulence at bottlenecks, reduce building evacuation times, produce emergent patterns, and increase the computational efficiency of an algorithm. We also propose using the Pareot Optimal Front as an efficient way of modelling optimal relationships between multiple objectives, and demonstrate its effectiveness by estimating optimal parameters for interactively defined combinations of the associated objectives. The proposed methodologies are general and can be applied to any steering algorithm using any set of performance criteria.
This video demonstrates some of the example results of parameter optimization.