Global Optimization

Global optimization aims to find the absolute best solution within a vast search space, a crucial task across numerous scientific and engineering disciplines. Current research emphasizes developing efficient algorithms that overcome challenges like high dimensionality, non-convexity, and the presence of numerous local optima, focusing on methods such as Bayesian optimization, evolutionary algorithms (including modifications like MRSO), and novel approaches leveraging generative models and large language models to guide the search. These advancements improve the accuracy and speed of finding optimal solutions for complex problems, impacting fields ranging from robotics and materials science to machine learning and engineering design.