有限评价预算下MOEA/D约束处理技术研究
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
摘要：
多目标优化问题的求解一直被认为是一个难题，特别是当多目标优化问题中存在约束时。因此，在MOPs的背景下，大多数的作品没有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的约束处理技术（CHTs）对MOEA/D的影响，MOEA/D是解决复杂的现实世界MOP时常用的MOP求解器。此外，我们介绍了一个简单而有效的CHT专注于探索的决策空间，三阶段惩罚。我们探索这些CHTs在MOEA/D上的两个模拟MOPs和六个分析MOPs（共八个）。这项工作的结果表明，虽然最好的CHT是依赖于问题的，我们新提出的三阶段惩罚实现了竞争力的结果和显着的性能方面的超体积值在硬模拟汽车设计MOP。
英文摘要：
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.
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