A non-domination criterion based metric that tracks the growth of an archive of non-dominated solutions over a few generations is proposed to generate a convergence curve for multi-objective evolutionary algorithms. It was observed that, similar to single-objective optimization problems, there were significant advances towards the Pareto optimal front in the early phase of evolution while relatively smaller improvements were obtained as the population matured. This convergence curve can be used to terminate the search to obtain a good trade-off between the computational cost and the quality of the solutions. Two analytical and two crashworthiness optimization problems were used to demonstrate the practical utility of the proposed metric. The paper also demonstrated a successful use of compute clusters for parallel processing to significantly reduce the clock time for optimization.
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