论文标题
我们是否真的需要在约束的进化多目标优化中使用约束违规？
Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?
论文作者
论文摘要
约束违规一直是设计进化多目标优化算法的基础，以解决约束的多目标优化问题。但是，在现实世界的黑盒优化方案中，违规的约束违规并不罕见。目前尚不清楚现有的约束进化多目标优化算法（其环境选择机制是建立在约束违规的基础上）是否在限制功能的表述未知时仍然可以工作。考虑到这一考虑，本文提取了四种广泛使用的约束进化多目标优化算法作为基线，并开发了相应的变体，这些变体通过清晰的值代替约束违规。从我们对合成和现实基准测试问题的实验中，我们发现，当不使用约束违规来指导环境选择时，所选算法的性能并没有显着影响。
Constraint violation has been a building block to design evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems. However, it is not uncommon that the constraint violation is hardly approachable in real-world black-box optimization scenarios. It is unclear that whether the existing constrained evolutionary multi-objective optimization algorithms, whose environmental selection mechanism are built upon the constraint violation, can still work or not when the formulations of the constraint functions are unknown. Bearing this consideration in mind, this paper picks up four widely used constrained evolutionary multi-objective optimization algorithms as the baseline and develop the corresponding variants that replace the constraint violation by a crisp value. From our experiments on both synthetic and real-world benchmark test problems, we find that the performance of the selected algorithms have not been significantly influenced when the constraint violation is not used to guide the environmental selection.
