Authors: | Naujoks, Boris |
Title: | Design and tuning of an evolutionary multiobjective optimisation algorithm |
Language (ISO): | en |
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. |
Subject Headings: | Evolutionary multiobjective optimisation Hypervolume Stopping criteria Sequential parameter optimisation |
URI: | http://hdl.handle.net/2003/27678 http://dx.doi.org/10.17877/DE290R-13412 |
Issue Date: | 2011-04-06 |
Appears in Collections: | LS 11 |
Files in This Item:
File | Description | Size | Format | |
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dissertation.pdf | DNB | 5.45 MB | Adobe PDF | View/Open |
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