Evolutionary approaches for solving bilevel optimization problems

PhD in Computer Science / Mechanical Engineering

Project description: 

Bilevel optimization involves solving an upper level "leader" optimization problem subject to the optimality of a nested lower level "follower" problem. Both levels have their associated objective(s), variable(s) and constraint(s) with interaction between them. Such problems model real-life scenarios where there exist decision-makers are multiple hierarchical levels. A number of practical applications in the diverse fields such as logistics, transportation, engineering, finance and others are suitable to be modelled as bilevel problems due to their inherent nested structure.

Bilevel optimization problems contain several characteristic challenges not found in standard (single level) problems, such as (a) requirement of a large number of design evaluations to achieve good solutions (b) difficult-to-navigate problem landscape (c) conflict between the leader and follower problems that can mislead search algorithms and (d) inapplicability of standard performance metrics for assessment and benchmarking. 

This research aims to develop methods to address the above challenges and deal with bilevel optimization with using low computational expense. The developed methods will be primarily metaheuristic (such as evolutionary algorithms) in nature, complemented by improvements in mathematical formulations and use of metamodels.

Required Background: 

Good programming (Matlab, C/C++) 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.

Eligibility:

Must meet UNSW admission criteria and English Language requirements. Scholarships of AUD 35,000 per annum are available for PhD students joining UNSW Canberra who achieved H1 /High Distinction in their UG program and/or have completed a Masters by Research. For more details and eligibility, visit https://www.unsw.adfa.edu.au/study/scholarship/international-postgraduate-research-scholarships

Contact:

Please send electronic copies of CV and transcripts to Dr Hemant Kumar Singh (h.singh@adfa.edu.au).

Multidisciplinary Design Optimization (MDO) Group, UNSW Canberra (http://mdolab.net/)

Supervisors