论文标题
多目标黑框优化中的约束处理
Handling of constraints in multiobjective blackbox optimization
论文作者
论文摘要
这项工作提出了将两种新约束处理方法集成到Blackbox约束多目标优化算法DMULTI-MADS中，这是单目标受约束优化的网格自适应直接搜索（MADS）算法的扩展。约束汇总为单个约束违规函数，该功能要么在两阶段方法中使用，因此，如果在改进当前解决方案集之前或进行渐进式屏障方法，则优先考虑可行点的研究，其中任何其约束违规功能值的试验点均高于阈值。该阈值沿迭代逐渐降低。与单目标情况一样，证明这两个变体会产生可行和/或不可行的序列，这些序列在可行情况下会收敛到一组局部帕累托最佳点，或者在不可避免的情况下，根据约束违规功能，可以根据clarke固定点进行clarke固定点。计算实验表明，这两种方法与其他最先进的算法具有竞争力。
This work proposes the integration of two new constraint-handling approaches into the blackbox constrained multiobjective optimization algorithm DMulti-MADS, an extension of the Mesh Adaptive Direct Search (MADS) algorithm for single-objective constrained optimization. The constraints are aggregated into a single constraint violation function which is used either in a two-phase approach, where research of a feasible point is prioritized if not available before improving the current solution set, or in a progressive barrier approach, where any trial point whose constraint violation function values are above a threshold are rejected. This threshold is progressively decreased along the iterations. As in the single-objective case, it is proved that these two variants generate feasible and/or infeasible sequences which converge either in the feasible case to a set of local Pareto optimal points or in the infeasible case to Clarke stationary points according to the constraint violation function. Computational experiments show that these two approaches are competitive with other state-of-the-art algorithms.
