Optimization and Design
Optimization is an integral part of any design or decision making process. Increased computing power, availability of accurate numerical models and the need to solve today's complex problems have led to significant growth of the field in recent years. The members of the group had been working on a number of theoretical and applied areas ranging from decision making in presence of uncertainty, identification of solutions of interest, development of efficient optimization methods for the solution of computationally expensive optimization problems, disruption modelling of production processes etc.
The group has developed cutting-edge technologies that are can deal with complex practical optimization problems (simulation based optimization) involving computationally expensive solvers. The mix of expertise in the group offers the unique capability to deliver innovative and cost-effective solutions across a range of problem domains.
The group currently consists of 13 academics, 1 postdoctoral researcher and well over 10 research students. There are also a large number of international and local collaborators. In the past five years, the group has secured grants well over $1.5 million from Australian Research Council alone and produced over 200 peer reviewed publications in premier outlets.
Further details can be found from www.mdolab.net.
FUNDED RESEARCH PROJECTS
- ARC Discovery Project (2017-2019): Reactive Planning under Disruptions and Dynamic Changes.
- ARC Discovery Project (2015-2017): Robust Configuration of Evolutionary Algorithms.
- ARC Discovery Project (2013-2015): Intriguing Aerodynamics of Bees, Hoverflies and Beyond.
- ARC Future Fellowship (2012-2016): Development of Methods and Algorithms to Support Multidisciplinary Optimization.
- Australia-Germany Joint Research Cooperation Scheme (2017-2018): Identification of Solutions of Interest to Aid Evolutionary Multi-objective Optimization and Decision-making, Universities Australia and DAAD.
CURRENT RESEARCH PROJECTS
- Development of Optimization Methods for Many-objective Optimization
- Development of Optimization Methods for Computationally Expensive Optimization Problems using Black Box /Iterative Solvers.
- Optimum Design of Renewable Energy Systems
- Optimum Design of Novel Materials
- Knowledge-based Evolutionary Multi-objective Approach for Stochastic Extended Resource Investment Project Scheduling Problems
- Maximum Entropy Analysis of Non-Equilibrium Flow Systems and Flow Networks
- Managing Real-time Demand Fluctuation
- Modelling Disruption Management
- Differential Evolution with Dynamic Parameters Selection