Design and tuning of an evolutionary multiobjective optimisation algorithm
| dc.contributor.advisor | Rudolph, Günter | |
| dc.contributor.author | Naujoks, Boris | |
| dc.contributor.referee | Jin, Yaochu | |
| dc.date.accepted | 2011-03-29 | |
| dc.date.accessioned | 2011-04-06T13:58:22Z | |
| dc.date.available | 2011-04-06T13:58:22Z | |
| dc.date.issued | 2011-04-06 | |
| dc.description.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. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/27678 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-13412 | |
| dc.language.iso | en | de |
| dc.subject | Evolutionary multiobjective optimisation | en |
| dc.subject | Hypervolume | en |
| dc.subject | Stopping criteria | en |
| dc.subject | Sequential parameter optimisation | en |
| dc.subject.ddc | 004 | |
| dc.title | Design and tuning of an evolutionary multiobjective optimisation algorithm | en |
| dc.type | Text | de |
| dc.type.publicationtype | doctoralThesis | de |
| dcterms.accessRights | open access |