The Impact of Population Size, Number of Children, and Number of Reference Points on the Performance of NSGA-III

We investigate the impact of three control parameters the population size $$\mu $$, the number of children $$\lambda $$, and the number of reference points H on the performance of Nondominated Sorting Genetic Algorithm III NSGA-III. In the past few years, many efficient Multi-Objective Evolutionary Algorithms MOEAs for Many-Objective Optimization Problems MaOPs have been proposed, but their control parameters have been poorly analyzed. The recently proposed NSGA-III is one of most promising MOEAs for MaOPs. It is widely believed that NSGA-III is almost parameter-less and requires setting only one control parameter H, and the value of $$\mu $$ and $$\lambda $$ can be set to $$\mu = \lambda \approx H$$ as described in the original NSGA-III paper. However, the experimental results in this paper show that suitable parameter settings of $$\mu $$, $$\lambda $$, and H values differ from each other as well as their widely used parameter settings. Also, the performance of NSGA-III significantly depends on them. Thus, the usually used parameter settings of NSGA-III i.e., $$\mu = \lambda \approx H$$ might be unsuitable in many cases, and $$\mu $$, $$\lambda $$, and H require a particular parameter tuning to realize the best performance of NSGA-III.