We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution within some distance to the intended solution may be implemented. The aim is to find a robust solution, i.e., one where the worst possible solution nearby still performs as well as possible. Problems of this type exhibit another maximisation layer to find the worst case solution within the minimisation level of finding a robust solution, which makes them harder to solve than classic global optimisation problems. So far, only few methods have been provided that can be applied to black-box problems with implementation uncertainty. We improve upon existing techniques by introducing a novel particle swarm based framework which adapts elements of previous approaches, combining them with new features in order to generate more effective techniques. In computational experiments, we find that our new method outperforms state of the art comparator heuristics in almost 80% of cases.

中文翻译：

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用于具有实现不确定性的鲁棒优化的粒子群元启发式算法

我们考虑不确定性下的全局非凸优化问题。在这种情况下，不可能准确地实施所需的解决方案。相反，可以实现与预期解决方案相距一定距离内的任何其他解决方案。目的是找到一个稳健的解决方案，即附近最差的解决方案仍然表现得尽可能好。这种类型的问题表现出另一个最大化层，以在找到稳健解决方案的最小化级别内找到最坏情况的解决方案，这使得它们比经典的全局优化问题更难解决。到目前为止，只提供了很少的方法可以应用于具有实现不确定性的黑盒问题。我们通过引入一种新的基于粒子群的框架来改进现有技术，该框架适应了以前方法的元素，将它们与新功能相结合，以产生更有效的技术。在计算实验中，我们发现我们的新方法在几乎 80% 的情况下优于最先进的比较器启发式方法。