De Kleermaeker, S.H. (2000) Genetic algorithms in multiobjective design optimisation. Master's Thesis / Essay, Mathematics.
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Abstract
Optimisation is a challenge for computerized multidisciplinary design. With multidisciplinary design optmisation there are several objectives to optimise, originating from different disciplines. Therefore no obvious method to splice the multiple objectives into a single objective exists. Multiobjective optimisation is a method that can deal with multiple objectives. The genetic algorithm is a global optimisation method, which is inspired by natural evolution. It uses a population of solutions that evolves to better solutions with help of recombination and mutation. A genetic algorithm can cope with a design space that is, for example, discontinous or convex. That and the global character of the genetic algorithm make it a suitable algorithm to solve multiobjective optimisation problems. In this report an introduction to optimisation problems and genetic algorithms is given. Furthermore, the results are shown of two multiobjective optimisation problems: parameter choice in time integration methods and airfoil design.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Mathematics |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:29 |
| Last Modified: | 15 Feb 2018 07:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/8764 |
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