A Simple Yet Effective Approach to Robust Optimization Over Time

TL;DR

This paper introduces a simple, sampling-based method for robust optimization over time, demonstrating theoretical guarantees and superior performance compared to existing particle swarm optimization approaches.

Contribution

The paper proposes a novel random sampling approach for ROOT, providing theoretical analysis and empirical evidence of its effectiveness over current methods.

Findings

Outperforms state-of-the-art ROOT algorithms

Provides theoretical guarantees for the sampling method

Demonstrates significant empirical improvements

Abstract

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.