Constraint handling in efficient global optimization
Constraint handling in efficient global optimization
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DOI：
10.1145/3071178.3071278
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发表时间：
2017-07
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通讯作者：
Samineh Bagheri;W. Konen;R. Allmendinger;J. Branke;K. Deb;J. Fieldsend;D. Quagliarella;Karthik Sindhya
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作者：
Samineh Bagheri;W. Konen;R. Allmendinger;J. Branke;K. Deb;J. Fieldsend;D. Quagliarella;Karthik Sindhya
现实世界的优化问题通常受制于几个约束条件，这些约束条件在成本或时间方面的评估是昂贵的。尽管在使用代理模型进行昂贵的优化任务方面投入了大量的努力，但没有多少强大的代理辅助算法可以解决具有挑战性的约束问题。高效全局优化算法（EGO）是一种基于kriging的代理辅助算法。它最初被提出用于解决无约束问题，后来被修改为解决有约束问题。然而，这些类型的算法仍然存在几个问题，主要是：(1)早期停滞，(2)多个活动约束的问题，(3)频繁崩溃。在这项工作中，我们引入了一种新的基于ego的算法，该算法试图克服Kriging优化算法的这些常见问题。我们将提出的算法应用于g函数组[16]中d≤4维的问题和一个翼型外形实例。
Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes. In this work, we introduce a new EGO-based algorithm which tries to overcome these common issues with Kriging optimization algorithms. We apply the proposed algorithm on problems with dimension d ≤ 4 from the G-function suite [16] and on an airfoil shape example.