Surrogate assisted optimization methods to deal with multiple solution representation schemes

Optimization problems often involve solutions that are represented as chromosomes or trees. The underlying variables can be continuous, integers, binary or categorical in nature. Current class of surrogate assisted optimization algorithms are not efficient in dealing with such classes of solution representations.

This research aims to develop surrogate models and surrogate assisted optimization methods that can deal with such classes of problems where solutions are represented using any of the above forms. Such problems are frequently encountered in concept design. The research has applications in wide range of practical problems in engineering, renewable energy, transport, process optimization, to name a few.  

Required Background: Good Python programming 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. 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 Professor Tapabrata Ray at t.ray@adfa.edu.au. Include a copy of your CV and your academic transcript(s).