EVOLVE International Conference

EVOLVE 2014 July 1-4
Beijing, China


Optimisation Under Uncertainty: a Natural Bridge Between Probabilities and Evolutionary Computation

Instructor Prof. Massimiliano Vasile


Nowadays, the use of computer models has become an essential part of any decision process and design methodology. It forms the backbone of Model-Based System Engineering and of the concept of Virtual Prototyping (VP). Models are however incomplete representations of reality and are affected by uncertainties of different nature.
Not integrating a measure of these uncertainties in the evaluation of the design budgets (or performance indicators) during the optimisation of systems and components provides unreliable decisions on product quality and reliability.  A common approach to account for uncertainties in system design is to add safety margins to already optimised solutions. These margins are generally defined through experience and historical data rather than through a propagation of uncertainty through a system model. However, it can be shown that the use of predefined design margins applied to pre-optimised solutions can lead to an overestimation of the system budgets or of its reliability. In fact, optimised solutions can be very sensitive and poorly resilient to uncertainty and the a posteriori introduction of margins can result in suboptimal solutions.
A general tendency, from the design and control of manufacturing processes, to air traffic management, from decision making on multi-phase programmes to the control of the ascent trajectory of a rocket, is to introduce the quantification of uncertainty (UQ) directly in the optimisation process. This combination, however, significantly increases the complexity of the optimisation problem and dedicated techniques are required.

This tutorial will present different ways to quantify (or model) uncertainty, using both Probability and Imprecise Probability theories, and to propagate uncertainty through system models.
The tutorial will then introduce some formulations of the Optimisation Under Uncertainty (OUU) problem along with some examples of solution.
Particular attention will be dedicated to worst-case scenario optimisation problems and their solution with Evolutionary Computation.


Massimiliano Vasile (biography)


Massimiliano Vasile is currently Professor of Space Systems Engineering in the Department of Mechanical & Aerospace Engineering at the University of Strathclyde. Previous to this, he was a Senior Lecturer in the Department of Aerospace Engineering and Head of Research for the Space Advanced Research Team at the University of Glasgow. Before starting his academic career in 2004, he was the first member of the ESA Advanced Concepts Team and initiator of the ACT research stream on global trajectory optimisation, mission analysis and biomimicry. His research interests include Computational Optimization, Robust Design and Optimization Under Uncertainty exploring the limits of computer science at solving highly complex problems in science and engineering.

He developed Direct Transcription by Finite Elements on Spectral Basis for optimal control, implemented in the ESA software DITAN for low-thrust trajectory design. He has worked on the global optimisation of space trajectories developing innovative single and multi-objective optimisation algorithms, and on the combination of optimisation and imprecise probabilities to mitigate the effect of uncertainty in decision making and autonomous planning. More recently he has undertaken extensive research on the development of effective techniques for asteroid deflection and manipulation. His research has been funded by the European Space Agency, the EPSRC, the Planetary Society and the European Commission. Prof Vasile is currently leading Stardust, an EU-funded international research and training network on active debris removal and asteroid manipulation.


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