Abstract

Over the last ten years, Multi-Objective Evolutionary Algorithms (MOEAs) 
have undergone tremendous development and have been successfully applied 
in various fields to solve complex problems that were formulated typically 
with two or three objectives. However, an increasingly expanding basis of 
our knowledge of natural processes and engineering requirements, and the 
need of taking into account sustainability indicators in order to compensate 
for the socio-environmental damage caused by the human activity, has 
increasingly led to formulation of the real world optimisation problems 
requiring a large number of management criteria. This has posed a serious 
challenge for traditional MOEAs, which are known to progressively lose 
their effectiveness as the number of performance criteria of a problem 
increases, and has led researchers to begin investigating the issues related 
to the evolutionary optimisation of problems with more than three objectives, 
usually referred to as Many-Objective Problems (MOP), and devising remedial measures.

Motivated by the need to provide researchers and practitioners interested 
in water resources engineering with an optimisation method to effectively 
solve problems formulated with many objectives, this thesis is an effort 
to advance in the field of Evolutionary Many-objective Optimisation . In order to 
meet this ambitious goal, following an attentive review and analysis of the issues 
arising in this setting, a new methodology that can be easily incorporated into 
existing MOEAs is presented. Extensive experiments suggest that the methodology 
proposed improves significantly the performance of a state-of-the-art algorithm 
and suggest that other MOEAs could benefit to a larger extent from it. In addition, 
a novel methodological framework is developed providing formal tools to analyse the 
theoretical properties of the methodology introduced.

The relevance to the water resources engineering field of the optimisation methodology 
developed was illustrated through a case study dealing with the automatic calibration 
of an urban drainage model. A novel calibration framework, enabling the concurrent 
consideration of spatially distributed temporal time series, was developed leveraging 
the optimisation method proposed, and successfully applied to the calibration of the 
model BEMUS, for a 25ha experimental urban catchment.