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MOEA/D with Random Partial Update Strategy
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-01-20 , DOI: arxiv-2001.06980
Yuri Lavinas, Claus Aranha, Marcelo Ladeira and Felipe Campelo

Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work we investigate a new, simpler partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D with relative improvement-based resource allocation. The results indicate that using the MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced.

中文翻译:

具有随机部分更新策略的 MOEA/D

最近关于资源分配的研究表明,在 MOEA/D 的背景下,某些子问题比其他子问题更重要,关注最相关的子问题可以持续提高该算法的性能。这些研究具有在算法的任何给定迭代中仅更新一小部分总体的共同特征。在这项工作中,我们研究了一种新的、更简单的部分更新策略,其中在每次迭代时选择解决方案的随机子集。使用这种新资源分配方法的 MOEA/D 的性能与标准 MOEA/D-DE 和具有相对改进的资源分配的 MOEA/D 的性能进行了实验比较。结果表明,将 MOEA/D 与这种新的部分更新策略结合使用可以提高 HV 和 IGD 值,
更新日期:2020-09-30
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