There's an increasing demand for engineers to make the 'best' possible design decisions while decreasing costs and at a faster pace. This requires knowledge of methods in design optimisation which have become an integral part of product design and decision-making activities when developing mechanical structures, devices or systems.
Traditional design optimisation relied on manually identifying the right combination of design variables. This process is time-consuming and involves a trial-and-error approach to satisfy design objectives. Design optimisation allows for thousands of designs to be experimented with on a computer using mathematical formulations and simulations to find the optimal design relative to a set of performance objectives and constraints. Maximising factors such as productivity, strength and reliability, optimisation techniques have helped automate and improve efficiencies while reducing costs in product design, manufacturing processes and project planning.
We're 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're running several externally funded projects which include the Australian Research Council (ARC) Discovery projects, Future Fellowship and the Endeavour Fellowship.
Our research is successfully applied across several engineering applications including but not limited to:
We collaborate with a wide range of university and industry partners including:
Rahi, K.H., Singh, H. and Ray, T., “Evolutionary algorithm embedded with bump-hunting for constrained design optimization,” ASME Journal of Mechanical Design, in press (available online), 2020.
Singh, H., “Understanding hypervolume behavior theoretically for benchmarking in evolutionary multi/many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol 24, issue 3, pp. 603-610, 2020.
Elsayed, S., Sarker, R., Essam, D., Coello Coello, C. Evolutionary Approach for Large-Scale Mine Scheduling. Information Sciences, vol. 523, pp. 77- 90, 2020
Singh, H., Bhattacharjee, K.S., and Ray, T., “Distance based subset selection for benchmarking in evolutionary multi/many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol 23, issue 5, pp. 904-912, 2019.
Habib, A., Singh, H., Chugh, T., Ray, T., and Miettinen, K., “A multiple surrogate assisted decomposition based hybrid evolutionary algorithm for expensive multi/many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 23, issue 6, pp. 1000-1014, 2019.
Bhattacharjee, K.S., Singh, H. and Ray, T., “Multiple surrogate assisted many-objective optimization for engineering design,” ASME Journal of Mechanical Design, 40(5), 2018.
Asafuddoula, M., Singh, H. and Ray, T., “An enhanced decomposition based evolutionary algorithm with adaptive reference vectors,” IEEE Transactions on Cybernetics, vol. 48, issue 8, pp. 2321-2334, 2018.
Bhattacharjee, K.S., Singh, H., Ryan, M., and Ray, T., “Bridging the gap: Many-objective optimization and informed decision-making,” IEEE Transactions on Evolutionary Computation, vol. 21, issue 5, pp. 813-820, 2017.
Branke, J., Asafuddoula, M., Bhattacharjee, K.S., Ray, T., “Efficient Use of Partially Converged Simulations in Evolutionary Optimization,” IEEE Transactions on Evolutionary Computation, vol. 21, issue 1, pp. 52-64, 2017.
Asafuddoula, M., Ray, T. and Sarker, R., “A decomposition based evolutionary algorithm for many objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 19, issue 3, pp. 445-460, 2015.
Singh, H., Ray, T. and Sarker, R., “Optimum oil production planning using infeasibility driven evolutionary algorithm,” Evolutionary Computation, vol. 21, no. 1, pp. 65-82, 2013.
Mistree, F., W.F. Smith, B. Bras, J.K. Allen, and D. Muster, Decision-Based Design: A Contemporary Paradigm for Ship Design. Transactions SNAME, 1990. 98: p. 565-597
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:
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.
PhD projects are available on an ongoing basis in the field of evolutionary computation and design optimisation. Utilising principles of optimal design, topics include:
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.