Interactive visual analysis of multiobjective optimizations

Optimization problems are typically addressed by purely automatic approaches. For multi-objective problems, however, a single best solution often does not exist. In this case, it is necessary to analyze trade-offs between many conflicting goals within a given application context. This poster describes an approach that tightly integrates automatic algorithms for multi-objective optimization and interactive multivariate visualizations. Ad-hoc selections support a flexible definition of input data for subsequent algorithms. These algorithms in turn represent their result as derived data attributes that can be assigned to visualizations or be used as a basis for further selections (e.g., to constrain the result set). This enables a guided search that still involves the knowledge of domain experts. We describe our approach in the context of multi-run simulation data from the application domain of car engine design.