论文标题
在两个不同的优化方案下对MOEA/D的控制参数的分析
An Analysis of Control Parameters of MOEA/D Under Two Different Optimization Scenarios
论文作者
论文摘要
在搜索过程中发现的所有非主导解决方案的无界外部存档（UEA）经常用于评估最近的研究中多目标进化算法（MOEAS）的性能。最近的一项基准测试研究还表明，当将UEA纳入MOEA/D中时，基于分解的MOEA（MOEA/D）与最先进的MOEAS具有竞争力。但是，尚未进行使用UEA的MOEA/D参数研究。因此，目前尚不清楚控制参数设置如何影响MOEA/D对UEA的性能。在本文中，我们在两个绩效评估方案下对MOEA/D的控制参数进行了分析。一个是最终人口场景，基于最终人群中所有非主导解决方案进行MOEAS的性能评估，而另一个是UEA场景减少的，该方案基于UEA的预先指定数量的非主导解决方案。本文研究的MOEA/D的控制参数包括种群大小，标量功能以及基于罚款的边界交叉点（PBI）功能的惩罚参数。实验结果表明，三个控制参数的合适设置显着取决于选择优化方案。我们还分析了每种情况最佳参数设置完全不同的原因。
An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evolutionary algorithms (MOEAs) in recent studies. A recent benchmarking study also shows that decomposition-based MOEA (MOEA/D) is competitive with state-of-the-art MOEAs when the UEA is incorporated into MOEA/D. However, a parameter study of MOEA/D using the UEA has not yet been performed. Thus, it is unclear how control parameter settings influence the performance of MOEA/D with the UEA. In this paper, we present an analysis of control parameters of MOEA/D under two performance evaluation scenarios. One is a final population scenario where the performance assessment of MOEAs is performed based on all nondominated solutions in the final population, and the other is a reduced UEA scenario where it is based on a pre-specified number of selected nondominated solutions from the UEA. Control parameters of MOEA/D investigated in this paper include the population size, scalarizing functions, and the penalty parameter of the penalty-based boundary intersection (PBI) function. Experimental results indicate that suitable settings of the three control parameters significantly depend on the choice of an optimization scenario. We also analyze the reason why the best parameter setting is totally different for each scenario.
