Modular Optimization Framework

Modular optimization frameworks aim to improve the efficiency and transparency of optimization algorithms by breaking them down into interchangeable modules. Current research focuses on quantifying the impact of individual modules and their interactions, developing explainable benchmarking methods to understand algorithm performance across diverse scenarios, and applying these frameworks to various problems, including black-box optimization, image recognition, and integer programming. This modular approach facilitates algorithm design, comparison, and application, ultimately leading to more efficient and robust solutions for complex optimization problems in diverse scientific and engineering domains.