In this paper, we investigate the issue of convergence in multiobjective optimization problems when using a Multi-Objective Genetic Algorithm (MOGA) to determine the set of Pareto optimal solutions. Additionally, given a Pareto set for a multi-objective problem, the mapping between the performance and design space is studied to determine design variable configurations for a given set of performance specifications. The advantage of this study is that the design variable information is obtained without having to repeat system analyses. The tools developed in this paper have been applied to develop a Technical Feasibility Model (TFM) used by General Motors as well as a simple multiobjective optimization problem in this paper. The multi-objective problem is primarily used to illustrate the developed methodology.

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