Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks

2026-08-13

AI Summary

Existing global optimization benchmarks predominantly rely on low-dimensional, outdated analytical functions that fail to capture the complexities of high-dimensional black-box optimization encountered in modern machine learning. This work introduces, for the first time, a systematic formulation of black-box adversarial attacks as a large-scale, high-dimensional global optimization benchmark. The proposed framework enables comprehensive evaluation of diverse evolutionary algorithms and metaheuristic methods under realistic conditions. By bridging the gap between theoretical optimization and practical machine learning challenges, this study not only enhances the real-world relevance of optimization problems but also fosters deeper integration between optimization algorithm design and the demands of contemporary machine learning applications, thereby establishing a more representative and rigorous evaluation platform for future research.

Abstract

Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optimization methods. We argue that the tasks related to the black-box adversarial attack (BBAA) can serve as valuable global optimization benchmark in many-dimensional space. We demonstrate the efficiency of several types of evolutionary algorithms and other metaheuristics in solving example BBAA problems. Thus, we take a step towards convergence of global optimization methods to the challenges and needs that arise in the modern machine learning field.

Problem

• global optimization
• benchmarking
• black-box adversarial attacks
• high-dimensional optimization
• evolutionary algorithms

Innovation

• black-box adversarial attack
• global optimization
• benchmarking
• evolutionary algorithms
• metaheuristics