Lu, Ke (2017) Evolutionary multi-objective worst-case robust optimisation. PhD thesis, University of Warwick.

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Abstract

Many real-world problems are subject to uncertainty, and often solutions should not only be good, but also robust against environmental disturbances or deviations from the decision variables. While most papers dealing with robustness aim at finding solutions with a high expected performance given a distribution of the uncertainty, we examine the trade-off between the allowed deviations from the decision variables (tolerance level), and the worst case performance given the allowed deviations. In this research work, we suggest two multi-objective evolutionary algorithms to compute the available trade-offs between allowed tolerance level and worst-case quality of the solutions, and the tolerance level is defined as robustness which could also be the variations from parameters. Both algorithms are 2-level nested algorithms. While the first algorithm is point-based in the sense that the lower level computes a point of worst case for each upper level solution, the second algorithm is envelope-based, in the sense that the lower level computes a whole trade-off curve between worst-case fitness and tolerance level for each upper level solution.

Our problem can be considered as a special case of bi-level optimisation, which is computationally expensive, because each upper level solution is evaluated by calling a lower level optimiser. We propose and compare several strategies to improve the efficiency of both algorithms. Later, we also suggest surrogate-assisted algorithms to accelerate both algorithms.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Robust optimization, Uncertainty, Mathematical optimization, Evolutionary computation
Official Date:	September 2017
Dates:	Date Event September 2017 Submitted
Institution:	University of Warwick
Theses Department:	Warwick Business School
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Branke, Jürgen, 1969- ; Gülpinar, Nalân
Format of File:	pdf
Extent:	vi, 112 leaves : illustrations, charts
Language:	eng

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