Design and tuning of an evolutionary multiobjective optimisation algorithm
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Date
2011-04-06
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Abstract
In this cumulative thesis an approach to multiobjective evolutionary optimisation using
the hypervolume or the S-metric, respectively for selection is presented. This algorithm
is tested and compared to standard techniques on two-, three and more dimensional
objective spaces.
To decide upon the right time when to stop a stochastic optimisation run, the method
called online convergence detection is developed. This as well as the framework of
sequential parameter optimisation for evolutionary multiobjective optimisation algo-
rithms are general frameworks for different kinds of optimisation approaches. Both are
successfully coupled with the presented algorithm on different test cases, even industrial
ones from aerodynamics.
A chapter on diversity in decision and objective space completes this thesis, which ends
with an outlook on interesting research directions for the future.
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Keywords
Evolutionary multiobjective optimisation, Hypervolume, Stopping criteria, Sequential parameter optimisation