Over the past 2 decades, several multi-objective optimizers (MOOs) have been proposed to address the different aspects of multi-objective optimization problems (MOPs). Unfortunately, it has been observed that many of MOOs experiences performance degradation when applied over MOPs having a large number of decision variables and objective functions. Specially, the performance of MOOs rapidly decreases when the number of decision variables and objective functions increases by more than a hundred and three, respectively. To address the challenges caused by such special case of MOPs, some large-scale multi-objective optimization optimizers (L-MuOOs) and large-scale many-objective optimization optimizers (L-MaOOs) have been developed in the literature. Even after vast development in the direction of L-MuOOs and L-MaOOs, the supremacy of these optimizers has not been tested on real-world optimization problems containing a large number of decision variables and objectives such as large-scale many-objective software clustering problems (L-MaSCPs). In this study, the performance of nine L-MuOOs and L-MaOOs (i.e., S3-CMA-ES, LMOSCO, LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA) is evaluated and compared over five L-MaSCPs in terms of IGD, Hypervolume, and MQ metrics. The experimentation results show that the S3-CMA-ES and LMOSCO perform better compared to the LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA in most of the cases. The LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, and DREA, are the average performer, and H-RVEA is the worst performer.

在过去的20年中，已经提出了几种多目标优化器（MOO）来解决多目标优化问题（MOP）的不同方面。不幸的是，已经观察到，许多MOO应用于具有大量决策变量和目标函数的MOP时，性能会下降。特别是，当决策变量和目标函数的数量分别增加一百多个和三个时，MOO的性能会迅速下降。为了应对这种特殊情况的MOP带来的挑战，文献中已经开发了一些大型多目标优化优化器（L-MuOOs）和大型多目标优化优化器（L-MaOOs）。即使在L-MuOOs和L-MaOOs方向上取得了巨大发展之后，尚未针对包含大量决策变量和目标的现实世界优化问题（例如大规模多目标软件聚类问题（L-MaSCP））对这些优化器的优势进行过测试。在这项研究中，评估了9种L-MuOO和L-MaOO（即S3-CMA-ES，LMOSCO，LSMOF，LMEA，IDMOPSO，ADC-MaOO，NSGA-III，H-RVEA和DREA）的性能，在IGD，Hypervolume和MQ指标方面比较了五个L-MaSCP。实验结果表明，在大多数情况下，与LSMOF，LMEA，IDMOPSO，ADC-MaOO，NSGA-III，H-RVEA和DREA相比，S3-CMA-ES和LMOSCO的性能更好。LSMOF，LMEA，IDMOPSO，ADC-MaOO，NSGA-III和DREA是表现最差的，而H-RVEA是表现最差的。