论文标题
MOEA/D具有随机部分更新策略
MOEA/D with Random Partial Update Strategy
论文作者
论文摘要
关于资源分配的最新研究表明，在MOEA/D的背景下，某些子问题比其他子问题更重要，并且专注于最相关的问题可以一致地提高该算法的性能。这些研究具有在任何给定算法的迭代中仅更新一小部分人群的共同特征。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，其中在每次迭代中都会选择一个随机的解决方案。使用这种新的资源分配方法的MOEA/D的性能与标准MOEA/D-DE和MOEA/D的基于相对改进的资源分配进行了比较。结果表明，使用MOEA/D与这种新的部分更新策略一起，可以提高HV和IGD值，并在非主导的解决方案中更高比例，尤其是随着每次迭代时更新的解决方案的数量减少。
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.
