Constrained Optimization

Constrained optimization focuses on finding the best solution to a problem while adhering to limitations or restrictions. Current research emphasizes developing efficient algorithms, such as enhanced differential evolution, Bayesian neural networks, and novel interior-point methods, to solve increasingly complex problems across diverse domains, including those involving differential equations and high-dimensional spaces. These advancements are crucial for tackling real-world challenges in various fields, from energy systems and finance to machine learning and robotics, where optimal solutions must satisfy critical constraints. The development of robust and scalable methods for constrained optimization is driving progress in numerous scientific and engineering disciplines.