Active-set approaches are commonly used in algorithms for constrained numerical optimization. We propose that active-set techniques can beneficially be employed for evolutionary black-box optimization with explicit constraints and present an active-set evolution strategy. We experimentally evaluate its performance relative to those of several algorithms for constrained optimization and find that the active-set evolution strategy compares favourably for the problem set under consideration.

中文翻译：

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显式约束进化优化的活动集

活动集方法通常用于约束数值优化的算法中。我们建议活动集技术可以有益地用于具有显式约束的进化黑盒优化，并提出活动集进化策略。我们通过实验评估其相对于几种约束优化算法的性能，并发现活动集进化策略对于所考虑的问题集比较有利。