Multi-disciplinary and multi-objective design optimisation tools are used more and more in order to help CAE designers and managers in their quest for higher product quality and returns. The present paper illustrates the comparison between the results achieved by means of the MOGT (Multi-Objective Game Theory) and the MOGA (Multi-Objective Genetic Algorithm) optimisation strategies. The aim of the application consists in a construction machinery cab vibro-acoustic performance optimisation. 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. The recently developed results representation by means of SOM (Self-Organizing Maps) represents a powerful analysis tool. It allows a clear and fast qualitative comprehension of the relations between optimisation process design variables and objectives.
MOGA & MOGT Optimization Strategies and SOM Results representation
Miccoli G;
2006
Abstract
Multi-disciplinary and multi-objective design optimisation tools are used more and more in order to help CAE designers and managers in their quest for higher product quality and returns. The present paper illustrates the comparison between the results achieved by means of the MOGT (Multi-Objective Game Theory) and the MOGA (Multi-Objective Genetic Algorithm) optimisation strategies. The aim of the application consists in a construction machinery cab vibro-acoustic performance optimisation. 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. The recently developed results representation by means of SOM (Self-Organizing Maps) represents a powerful analysis tool. It allows a clear and fast qualitative comprehension of the relations between optimisation process design variables and objectives.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.