The performance of multiobjective algorithms varies across problems, making
it hard to develop new algorithms or apply existing ones to new problems. To
simplify the development and application of new multiobjective algorithms,
there has been an increasing interest in their automatic design from component
parts. These automatically designed metaheuristics can outperform their
human-developed counterparts. However, it is still uncertain what are the most
influential components leading to their performance improvement. This study
introduces a new methodology to investigate the effects of the final
configuration of an automatically designed algorithm. We apply this methodology
to a well-performing Multiobjective Evolutionary Algorithm Based on
Decomposition (MOEA/D) designed by the irace package on nine constrained
problems. We then contrast the impact of the algorithm components in terms of
their Search Trajectory Networks (STNs), the diversity of the population, and
the hypervolume. Our results indicate that the most influential components were
the restart and update strategies, with higher increments in performance and
more distinct metric values. Also, their relative influence depends on the
problem difficulty: not using the restart strategy was more influential in
problems where MOEA/D performs better; while the update strategy was more
influential in problems where MOEA/D performs the worst.

研究自动设计算法对多目标优化算法性能影响的新方法，发现重启和更新策略对性能改进影响最大，影响程度还与问题难度相关。