Bregant Luigi - Università di Trieste, Dipartimento di Ingegneria Meccanica
Miccoli Giuseppe - IMAMOTER Institute, National Research Council (C.N.R.)
Pediroda Valentino - Università di Trieste, Dipartimento di Ingegneria Meccanica
Seppi M. - IMAMOTER Institute, National Research Council (C.N.R.) |
For industrial problems, several optimisation strategies can be used to select candidates for evaluation, as for instance, Preliminary Exploration Methods, DOE, Local Refinement Methods, Special-Purpose Plug-ins or Multi-Objective Optimization Methods that include genetic algorithms and evolution strategies. In some cases, the best method to be used can be far from evident, or clear criteria for the choice can be missing.
The aim of the application consists in a multi-disciplinary optimisation of a construction machinery cab considering its vibro-acoustic performance. The multi-objective design optimisation code (modeFRONTIER) drives the analysis process flow taking into account the cab parameter structural modifications and carrying out the vibro-acoustic field optimisation. A 3D cavity representing the real cab has been modelled by means of a (ANSYS) FE structural mesh. Starting from the cab vibration load experimental acquisition, a (Sysnoise) BEM coupled analysis has been carried out in order to evaluate the cab inner vibro-acoustic field as a function of the physical properties of each structural element.
The present paper illustrates the comparison between the results achieved by means of MOGA (Multi Objective Genetic Algorithm) and MOGT (Multi Objective Game Theory) optimisation strategies. Optimisation run variables/objectives dependence and computation process logic have been evaluated in order to understand peculiarities and overcome limits of the methodologies. Previous works have partially evidenced some charcteristics and requirements of the different approaches. Due to some parameters definitions, a more clear comparison of the two methods was still unavailable. A complete view of the numerical optimisation problem and an exhaustive comparison between MOGA and MOGT have been achieved for the case considered.
After all the less tested and more innovating MOGT strategy shows itself to be a robust and fast multi objective optimisation tool too when combined with Evolutionary Algorithms. |
