A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty
A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty
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DOI：
10.1016/j.cor.2018.10.013
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发表时间：
2018-09
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通讯作者：
Martin Hughes;M. Goerigk;Michael Wright
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文献类型：
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作者：
Martin Hughes;M. Goerigk;Michael Wright
我们考虑具有实现不确定性的盒约束鲁棒优化问题。在这种情况下，决策者想要实现的解决方案可能会受到干扰。其目的是找到一个解决方案，在所有可能的扰动中，使最坏的可能表现最优化。在此之前，针对此设置仅开发了少数通用搜索方法。我们引入了一种新的全局搜索方法，基于放置一个最大的空超球。我们不假设对原始目标函数的结构有任何了解，使这种方法也适用于模拟优化设置。在计算实验中，我们证明了与最先进的方法相比，我们的方法具有很强的性能，这使得解决高维问题成为可能。
We consider box-constrained robust optimisation problems with implementation uncertainty. In this setting, the solution that a decision maker wants to implement may become perturbed. The aim is to find a solution that optimises the worst possible performance over all possible perturbances.Previously, only few generic search methods have been developed for this setting. We introduce a new approach for a global search, based on placing a largest empty hypersphere. We do not assume any knowledge on the structure of the original objective function, making this approach also viable for simulation-optimisation settings. In computational experiments we demonstrate a strong performance of our approach in comparison with state-of-the-art methods, which makes it possible to solve even high-dimensional problems.