Chance Constraint

Chance-constrained optimization addresses optimization problems where constraints involve stochastic elements, aiming to find solutions that satisfy these constraints with a specified probability. Current research focuses on developing efficient algorithms, such as Monte Carlo Tree Search and evolutionary multi-objective methods, often incorporating machine learning techniques like graph neural networks to handle complex problem structures and uncertainty. This field is crucial for addressing real-world problems in robotics, control systems, and resource allocation where safety and reliability under uncertainty are paramount, leading to improved performance and robustness in various applications.