Development of methods for solving large scale constrained optimization Problems

Designers are often faced with the need to solve large scale, computationally expensive constrained optimization problems. Small and often disconnected feasible patches, high underling dimensionality of the variable space and computationally expensive assessment of constraints pose significant challenges to population based stochastic optimization algorithms.

This project will focus on design of computationally efficient optimization algorithms to solve such classes of problems and demonstrate their performance through extensive benchmarking on analytical and real-world problems of interest.  

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).