Performance analysis of evolutionary multi-objective optimization methods in noisy environments

In this paper, we present performance comparisons between two popular elitism–based evolutionary multi-objective optimization algorithms -NSGA2 and SPEA2 in the presence of noise. Three test problems and six noise levels are employed in the research experiments. The results show that SPEA2 outperforms NSGA2 in the early generations. NSGA2, however, is superior during latter generations regardless of the level of noise presence in the problem.

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