Simulated Annealing

Simulated annealing is a probabilistic metaheuristic optimization technique that iteratively explores a solution space by accepting both improving and worsening solutions with probabilities dependent on a gradually decreasing "temperature" parameter. Current research focuses on enhancing its efficiency and applicability through hybrid approaches combining it with genetic algorithms, machine learning models (like XGBoost), and neuromorphic hardware, as well as developing theoretical frameworks for analyzing its behavior and optimizing its parameters. These advancements are improving its performance in diverse fields, including deep neural network analysis, materials science optimization, and resource allocation problems in logistics and computing, demonstrating its continued relevance for solving complex optimization challenges.