About the Project
Multiobjective optimisation refers to an optimisation scenario having more than one objective to be considered simultaneously. It exists ubiquitously. When we travel, we care about the time and the cost. When buying a car, we care about its performance and price. A major difference of multi-objective optimisation to single-objective optimisation (i.e., global optimisation) is that there exists no single optimal solution that can achieve the best for all the objectives, but rather a set of tradeoff solutions (called Pareto optimal front) which are not comparable to each other. The goal of multi-objective optimisation algorithms is to find a good representation of the Pareto optimal front, from which the decision maker can choose their most preferred one to deploy. A key issue in multi-objective optimisation is how to evaluate and compare solution sets generated by different optimisation algorithms. This is particularly challenging in practical scenarios where the decision maker’s preferences can be vague and practitioners may not be very familiar with multi-objective optimisation. The proposed research is to tackle this issue, aiming to develop more accessible and comprehensible evaluation metrics, and to help the decision maker to choose their favourite solution.
Eligibility: First or Upper Second Class Honours undergraduate degree and/or postgraduate degree with Distinction (or an international equivalent). We also consider applicants from diverse backgrounds that has provided them with equally rich relevant experience and knowledge. Full-time and part-time study modes are available.
We welcome applications from highly motivated prospective students with a background in Computer Science or Operational Research with an interest in evolutionary optimisation or multi-criteria decision making. A familiarity with multi-objective optimisation is desirable but not essential.
If your first language is not English and you have not studied in an English-speaking country, you will have to provide an English language qualification.
We want our PhD student cohorts to reflect our diverse society. UoB is therefore committed to widening the diversity of our PhD student cohorts. UoB studentships are open to all and we particularly welcome applications from under-represented groups, including, but not limited to BAME, disabled and neuro-diverse candidates. We also welcome applications for part-time study.
M. Li, T. Chen, X. Yao. How to evaluate solutions in Pareto-based search-based software engineering? A critical review and methodological guidance. IEEE Transactions on Software Engineering, 2020.
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