  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.
