具有一致随机自适应权值的MOEA/D
MOEA/D with Uniformly Randomly Adaptive Weights
摘要：
当使用基于分解的算法时，适当的权重集可能会提高最终解决方案的质量。一组均匀分布的权重通常会导致帕累托前沿的良好分布的解决方案。然而，这种方法有两个主要困难。首先，它可能会失败，这取决于问题的几何形状。其次，随着目标数量的增加，人口规模变得不灵活。在本文中，我们提出了MOEA/D与均匀随机自适应权重（MOEA/DURAW），它使用均匀随机方法作为子问题生成的方法，允许灵活的人口规模，即使在与许多目标问题。在进化过程中，MOEA/D-URAW添加和删除子问题作为人口的稀疏水平的函数。此外，而不是需要假设的帕累托前沿形状，我们的方法适应其权重的形状的问题在进化过程中。实验结果表明，使用WFG 41 -48问题类，具有不同的帕累托前沿形状，本方法提出了更好的或相等的结果，在77.5%的问题，从2到6个目标进行评估时，与国家的最先进的方法在文献中。
英文摘要：
When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Secondly, the population size becomes not flexible as the number of objectives increases. In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/DURAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems. During the evolutionary process, MOEA/D-URAW adds and removes subproblems as a function of the sparsity level of the population. Moreover, instead of requiring assumptions about the Pareto front shape, our method adapts its weights to the shape of the problem during the evolutionary process. Experimental results using WFG41-48 problem classes, with different Pareto front shapes, shows that the present method presents better or equal results in 77.5% of the problems evaluated from 2 to 6 objectives when compared with state-of-the-art methods in the literature.
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