Global Optimization Problem

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 and improving algorithms, including metaheuristics like basin hopping and evolutionary methods (e.g., leader-advocate-believer models), and integrating machine learning techniques (e.g., using neural networks and decision trees) to handle complex, high-dimensional, or black-box problems. These advancements are improving the efficiency and effectiveness of solving global optimization problems, impacting fields ranging from materials science and robotics to network optimization and disease control.