Recent Trends in Multiobjective Optimization and Optimal Control
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 15638
Special Issue Editors
Interests: dynamical systems; multiobjective optimization
Special Issues, Collections and Topics in MDPI journals
Interests: multiobjective optimal control; geometric integration; computational mechanics
Interests: multiobjective optimization; optimal control; fluid dynamics and flow control; dynamical systems; data-driven modeling; model order reduction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In many applications in the natural sciences, in engineering or in industry one has to optimize several objectives at the same time. A topical paradigm is the simultaneous optimization of safety, energy efficiency and comfort in the area of autonomous driving. In mathematical terms this leads to multiobjective optimization problems whose solution—the “optimal compromise” between the different objectives—is provided by the set of so-called Pareto optimal points.
Due to the increasing complexity of current technical innovations, interest in the field of multiobjective optimization has recently increased significantly. The purpose of this Special Issue is to highlight recent trends and significant advances in this area. Of interest in this context are topics including but not limited to:
- multiobjective optimal/model predictive/feedback control;
- multiobjective optimization with PDE constraints;
- many-objective optimization (i.e. the number of objectives is larger than 3);
- multiobjective optimization based on data, or in combination with mathematical models (e.g. for the problems mentioned above).
Approaches to the solution of these problems are e.g. provided by classical mathematical optimization methods, modern control strategies, techniques from model order reduction, evolutionary algorithms, robust and data-driven optimization or from machine learning. Each submission should either directly address or elaborate on the relevance for an application. However, the wide spectrum of applications is not limited and it could range from real-time applications in autonomous driving to control problems in fluid mechanics.
Prof. Dr. Michael Dellnitz
Prof. Dr. Sina Ober-Bloebaum
Dr. Sebastian Peitz
Guest Editors
Manuscript Submission Information
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Keywords
- multiobjective optimization
- multiobjective optimal control
- data based control
- reduced order modelling
- surrogate modelling
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