A Simple Yet Effective Approach to Robust Optimization Over Time
It addresses performance issues in robust optimization over time, offering a novel solution with proven improvements.
The paper tackles robust optimization over time by proposing a random sampling method, proving theoretical guarantees and showing it significantly outperforms state-of-the-art methods.
Robust optimization over time (ROOT) refers to an optimization problem where its performance is evaluated over a period of future time. Most of the existing algorithms use particle swarm optimization combined with another method which predicts future solutions to the optimization problem. We argue that this approach may perform subpar and suggest instead a method based on a random sampling of the search space. We prove its theoretical guarantees and show that it significantly outperforms the state-of-the-art methods for ROOT.