论文标题
探索MOEA/D的实际问题中的约束处理技术，评估预算有限
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
论文作者
论文摘要
寻找用于多目标优化（MOP）问题的良好解决方案被认为是一个困难的问题，尤其是在考虑具有约束的MOP时。因此，在MOP上下文中的大多数作品都不会深入探讨不同的约束如何影响MOP求解器的性能。在这里，我们专注于探索不同约束处理技术（CHT）对MOEA/D的影响，MOEA/D是在求解复杂的现实世界MOP时常用的MOP求解器。此外，我们引入了一个简单有效的CHT，专注于探索决策空间，即三阶段的惩罚。我们在MOEA/D中探索了两个模拟MOP和六个分析MOP（总共八个）。这项工作的结果表明，尽管最佳CHT是问题依赖性的，但我们的新提议的三阶段罚款可在硬模拟的汽车设计MOP中获得竞争成果和出色的性能。
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do not explore in-depth how different constraints affect the performance of MOP solvers. Here, we focus on exploring the effects of different Constraint Handling Techniques (CHTs) on MOEA/D, a commonly used MOP solver when solving complex real-world MOPs. Moreover, we introduce a simple and effective CHT focusing on the exploration of the decision space, the Three Stage Penalty. We explore each of these CHTs in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The results of this work indicate that while the best CHT is problem-dependent, our new proposed Three Stage Penalty achieves competitive results and remarkable performance in terms of hypervolume values in the hard simulated car design MOP.
