Large scale optimisation

This project is concerned with developing practical solutions for real-world large-scale optimisation.

Many real-world optimisation problems are large-scale, expensive to evaluate, and difficult to formulate, involving thousands of variables and constraints. Existing optimisation methods are ill-equipped to deal with large-scale problems, although they may perform well on smaller problems.

The project is involved in inter-disciplinary approaches integrating ideas from mathematical programming, meta-heuristics, and operations research. It will continue to build on our research strength in the area of large-scale optimisation using meta-heuristics. Our aim is to develop effective optimisation techniques for solving real-world large-scale optimisation problems that really matter to the practitioners.

The group publishes regularly in the top evolutionary computation journals and conferences, and has been actively involved in IEEE CIS taskforce on large-scale global optimisation, organising special sessions and competitions on this topic for the last few years. It attracts funding from both ARC Discovery and Linkage grants.

Key people

Staff

Students

  • Mohammad Nabi Omidvar
  • Borhan Kazimipour
  • Asad Mohammadi
  • Mohammad Haqqani

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Acknowledgement of country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nations on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business.