Evolutionary multi-objective optimisation in dynamic environments
Evolutionary Algorithms (EAs) have been applied successfully to a wide range of optimisation problems. However, many real-world problems possess multiple (and possibly conflicting) objectives that require frequent adaptation of optimised solutions. These dynamic attributes pose significant research challenges. In this project, the overarching goal is to design and evaluate a robust multi-objective optimisation algorithm for dynamic optimisation problem. An important phase of the project will be to undertake theoretical analysis of multi-objective EAs, by explicitly quantifying interactions between design variables and objectives in both static and dynamic environments.
Sun, Y, Kirley, M. and Halgamuge, S. (2017). Quantifying Variable Interactions in Continuous Optimization Problems. IEEE Transactions on Evolutionary Computation. 21(2):249-264.
Li,B., Li, J., Tang, K. and Yao, X . (2015). Many-Objective Evolutionary Algorithms: A Survey. ACM Computing Surveys, 48(1) 35
Leader: Michael Kirley
Computing and Information Systems
Optimisation of resources and infrastructure
evolutionary algorithms; optimisation