Optimisation & Design


We are developing cutting-edge, practical and efficient algorithms and frameworks to support multidisciplinary optimisation. These design optimisation methods, which provide solutions for a wide variety of design problems, address fundamental challenges and uncertainties in the optimisation process including: 

  • the presence of multiple conflicting performance criteria such as minimum cost, maximum reliability, and maximum strength 

  • the presence of several design variables and/or design constraints 

  • computationally expensive simulations 

  • highly non-linear programming or black-box response functions 

  • hierarchical objectives involving decision making at multiple levels. 

Our research is frequently published in leading journals such as the Institute of Electrical and Electronic Engineers (IEEE) Transactions on Evolutionary Computation Journal and the American Society of Mechanical Engineers (ASME) Journal of Mechanical Design. 

We are running several externally funded projects which include the Australian Research Council (ARC) Discovery projects, Future Fellowship and the Endeavour Fellowship.  

Competitive Advantage

  • A combination of expertise and experience in diverse fields such as evolutionary computation, engineering design and operations research.
  • Collaborations with leading researchers in the field globally. 

Successful Applications

Our research is successfully applied across several engineering applications including but not limited to: 

  • underwater vehicle design 
  • ship hull design 
  • scramjet geometries (Australian space research program) 
  • oil production planning 
  • wind-farm layout 
  • hybrid car controllers and energy management (Honda research institute). 
  • Evolutionary computation for robust multi-objective engineering design 

  • A novel and efficient approach for optimisation involving iterative solvers 

  • Solution of Interest identification based on recursive expected marginal utility 

  • Intelligent Algorithms for Portfolio Selection in Future Force Design 

  • Development of Methods and Algorithms to Support Multidisciplinary Optimisation 


Some members of our team hold prominent roles in international and Australasian conferences such as: 

  • Institute of Electrical and Electronic Engineers (IEEE) Congress on Evolutionary Computation (CEC)
  • Association for Computing Machinery (ACM) Genetic and Evolutionary Computation Conference (GECCO) 
  • IEEE Solid-State Circuits Society (SSCI) 
  • Australasian Conference on Artificial Life and Computational Intelligence (ACALCI) 
  • Intelligent Evolutionary Systems (IES) 
  • Evolutionary Multi-criteria Optimisation (EMO)  

We actively lead and participate in professional activities including IEEE Computational Intelligence Society local chapter, Taskforces, and editorial boards and reviewers for key journals in the field of engineering design and optimisation.   

Study With Us

PhD projects are available on an ongoing basis in the field of evolutionary computation and design optimisation. Utilising principles of optimal design, topics include: 

  • multi-objective optimisation 
  • constrained optimisation 
  • bilevel optimisation 
  • multi-criteria decision-making 
  • robust optimisation 
  • multi-fidelity optimisation.    

If you are interested in applying for PhD projects on the above topics, please contact Dr Hemant Singh.

The following course is available to fourth year undergraduate students: 

The content covered in this course applies to a diverse range of problems. Students who undertake this course have remarked on the fact that they learnt a new valuable tool, and several have applied their learnings in their final year thesis project.