Local Optimum

Local optima represent suboptimal solutions in optimization problems, hindering the search for global best solutions. Current research focuses on developing algorithms and strategies to escape these traps, including techniques like sharpness-aware minimization, dynamic regularization, and adaptive operator selection within evolutionary algorithms and Bayesian optimization frameworks. These advancements are crucial for improving the efficiency and effectiveness of optimization across diverse fields, from machine learning and federated learning to combinatorial optimization and physical design. The ability to reliably navigate complex landscapes and avoid premature convergence to inferior solutions has significant implications for the performance and scalability of numerous applications.