Speaker
Oliver Stein, Karlsruhe Institute of Technology
Abstract
After a geometrical introduction to multiobjective optimization and a discussion of common pitfalls, the talk explains a recently developed general framework for branch-and-bound methods in multiobjective optimization. For the first time in the multiobjective setting, it uses gap-based termination and node selection criteria. The method may be applied to continuous as well as to mixed-integer convex and nonconvex multiobjective problems. Numerical results for two and three objective functions illustrate this approach.