The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in performance of MOEA/D with RA. This study investigates the effects of MOEA/D with the Partial Update Strategy in an extensive set of MOPs to generate insights into correspondences of MOEA/D with the Partial Update and MOEA/D with small population size and big population size. Our work undertakes an in-depth analysis of the populational dynamics behaviour considering their final approximation Pareto sets, anytime hypervolume performance, attained regions and number of unique non-dominated solutions. Our results indicate that MOEA/D with Partial Update progresses with the search as fast as MOEA/D with small population size and explores the search space as MOEA/D with big population size. MOEA/D with Partial Update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique non-dominated solutions, the anytime performance and Empirical Attainment Function indicate.

中文翻译：

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基于分解的多目标优化算法的更快收敛

资源分配方法 (RA) 通过维持大量人口和每一代更新少量解决方案来提高 MOEA/D 的性能。然而，大多数关于 RA 的研究通常集中在不同资源分配指标的属性上。因此，仍然不确定导致 MOEA/D 与 RA 的性能增加的主要因素是什么。本研究调查了 MOEA/D 与部分更新策略在一组广泛的 MOP 中的影响，以深入了解 MOEA/D 与部分更新和 MOEA/D 与小人口规模和大人口规模的对应关系。我们的工作对人口动态行为进行了深入分析，考虑到它们的最终近似帕累托集、随时超容量性能、达到的区域和独特的非支配解决方案的数量。我们的结果表明，带有部分更新的 MOEA/D 与具有小种群规模的 MOEA/D 一样快地进行搜索，并且探索具有大种群规模的 MOEA/D 的搜索空间。具有部分更新的 MOEA/D 可以在大多数 MOP 中以更快的收敛速度缓解与种群规模选择相关的常见问题，如超容量和唯一非支配解的数量、随时性能和经验达到函数所示的结果所示。