Multiobjective Optimization of Non-Smooth PDE-Constrained Problems
Marco Bernreuther, Michael Dellnitz, Bennet Gebken, Georg M\"uller,, Sebastian Peitz, Konstantin Sonntag, Stefan Volkwein

TL;DR
This paper reviews recent advances in multiobjective optimization for non-smooth PDE-constrained problems, addressing challenges like computational complexity and non-smoothness in objectives or system dynamics.
Contribution
It provides an overview of new algorithms and methods developed for handling non-smoothness and computational challenges in multiobjective PDE-constrained optimization.
Findings
Summarizes recent algorithmic developments for non-smooth PDE optimization
Highlights challenges in computing Pareto sets with expensive models
Reports on progress within a specific research project
Abstract
Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control - potentially with non-smoothness both on the level of the objectives or in the system dynamics. This results in new challenges such as dealing with expensive models (e.g., governed by partial differential equations (PDEs)) and developing dedicated algorithms handling the non-smoothness. Since in contrast to single-objective optimization, the Pareto set generally consists of an infinite number of…
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Taxonomy
TopicsAdvanced Control Systems Optimization · Process Optimization and Integration · Probabilistic and Robust Engineering Design
