Particle swarm metaheuristics for robust optimisation with implementation uncertainty
Particle swarm metaheuristics for robust optimisation with implementation uncertainty
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DOI：
10.1016/j.cor.2020.104998
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发表时间：
2020-03
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通讯作者：
Martin Hughes;M. Goerigk;Trivikram Dokka
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作者：
Martin Hughes;M. Goerigk;Trivikram Dokka
我们考虑了不确定条件下的全局非凸优化问题。在这种情况下，不可能完全实现所需的解决方案。取而代之的是，可以实现距离预期解决方案一定距离内的任何其他解决方案。其目的是找到一个健壮的解，即附近最坏的可能解仍然表现得和可能的解一样好的解。这类问题展示了另一个最大化层，以在寻找健壮解的最小化水平内找到最坏的解，这使得它们比经典的全局优化问题更难求解。到目前为止，只有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的方法，该框架适应了以前方法的元素，并将它们与新的特征相结合，以产生更有效的方法。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。
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 methods, combining them with new features in order to generate a more effective approach. In computational experiments, we find that our new method outperforms state of the art comparator heuristics in almost 80% of cases.