Development of optimization methods for solving computationally expensive problems

Optimization problems involve maximization/minimization of certain performance objectives such as maximization of strength, maximization of fuel efficiency, minimization of weight, etc. This is done by parameterizing the model of the design and searching systematically for the combination of parameters that will yield the best performance. During the process of optimization, a large number of designs may need to be evaluated to achieve a near-optimal or satisfactory design. However, this becomes impractical when the evaluation of each design is computationally expensive. For example, when the design performance evaluation involves time-consuming simulations such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), etc.  

This research aims to develop methods that can significantly reduce the number of evaluations required during the optimization to achieve competitive results. The focus will be metaheuristic approaches (such as evolutionary algorithms) coupled with surrogate modelling and management strategies. Various problems involving practical challenges such as presence of multiple conflicting performance objective and design constraints will be considered. The research has applications in wide range of practical problems in engineering, renewable energy, transport, space-research, to name a few.  

Required Background:

• Good programming (e.g. Matlab/Python) and analytical skills, preferably with a Masters Degree in Engineering / Computer Science
• Prior research experience in optimization is desirable but not necessary
• Demonstrated competence in academic writing and oral presentation skills will be beneficial
  

You can find more details of the research conducted in our Multidisciplinary Design Optimization (MDO) group at http://www.mdolab.net/. Please feel free to reach out and discuss regarding this project, or have a discussion about other potential topics for undertaking Masters (research) or PhD with us.   

How to Apply

Express your interest in this project by emailing Associate Professor Hemant Kumar Singh at h.singh@unsw.edu.au. Include a copy of your CV and your academic transcript(s).