|Auteur||Filomeno Coelho, Rajan (email@example.com)|
|Titre||Multicriteria Optimization with Expert Rules for Mechanical Design|
|Département||F509 - Faculté des sciences appliquées - Construction (firstname.lastname@example.org)|
|Intitulé du diplôme||Doctorat en sciences appliquées|
|Date de défense||2004-04-01|
Breitkopf, Piotr (Membre du jury/Committee Member)
De Wilde, Patrick (Membre du jury/Committee Member)
Delchambre, Alain (Membre du jury/Committee Member)
Papadrakakis, Manolis (Membre du jury/Committee Member)
Remouchamps, Alain (Membre du jury/Committee Member)
Warzée, Guy (Membre du jury/Committee Member)
Vincke, Philippe (Président du jury/Committee Chair)
Bersini, Hugues (Promoteur/Director)
Bouillard, Philippe (Promoteur/Director)
|Mots-clés||structural optimization, evolutionary algorithms, mechanical design|
|Résumé||Though lots of numerical methods have been proposed in the literature to optimize me-chanical structures at the final stage of the design process, few designers use these tools since the first stage. However, a minor modification at the first step can bring significant change to the global performances of the structure. Usually, during the initial stage, models are based on theoretical and empirical equations, which are often characterized by mixed variables: continuous (e.g. geometrical dimensions), discrete (e.g. the cross section of a beam available in a catalogue) and/or integer (e.g. the number of layers in a composite material). Furthermore, the functions involved may be non differentiable, or even discontinuous. Therefore, classical algorithms based on the computation of sensi-tivities are no more applicable.
Consequently, to solve these problems, the most wide-spread meta-heuristic methods are evolutionary algorithms (EAs), which work as follows: the best individuals among an initial population of randomly generated potential solutions are favoured and com-bined (by specific operators like crossover and mutation) in order to create potentially better individuals at the next generation. The creation of new generations is repeated till the convergence is reached. The ability of EAs to explore widely the design space is useful to solve single-objective unconstrained optimization problems, because it gener-ally prevents from getting trapped into a local optimum, but it is also well known that they do not perform very efficiently in the presence of constraints. Furthermore, in many industrial applications, multiple objectives are pursued together.
Therefore, to take into account the constrained and multicriteria aspects of optimization problems in EAs, a new method called PAMUC (Preferences Applied to MUltiobjectiv-ity and Constraints) has been proposed in this dissertation. First the user has to assign weights to the m objectives. Then, an additional objective function is built by linearly aggregating the normalized constraints. Finally, a multicriteria decision aid method, PROMETHEE II, is used in order to rank the individuals of the population following the m+1 objectives.
PAMUC has been validated on standard multiobjective test cases, as well as on the pa-rametrical optimization of the purge valve and the feed valve of the Vinci engine, both designed by Techspace Aero for launcher Ariane 5.
The second step of the thesis consists in incorporating an inference engine within the optimization scheme in order to take expert rules into account. First, information about conception and design is collected among engineers expert in a specific domain. In the case of the valves designed by Techspace Aero, the expert rules are rules of thumb based upon experience, and related to the leakages, the choice of the materials for the different parts of the structure, etc. Then, each potential design generated by the EA is tested and repaired (with a given probability) according to the user-defined rules. This approach seems very efficient in reducing the size of the search space and guiding the EA towards the global feasible optimum.