Simulated Annealing (SA) is a metaheuristic technique grounded in the annealing process of metallurgy, renowned for its simplicity and effective performance. Despite these appealing attributes, SA faces significant limitations, including premature convergence. Conversely, Japanese swordsmithing involves a labor-intensive method for crafting high-quality blades from impure raw metals, where smiths repeatedly fold and reheat the metal to eliminate impurities and defects. This chapter explains an enhanced version of the SA algorithm, where a population of agents is utilized. Each agent executes a search strategy derived from a modified SA framework. The improved algorithm incorporates two novel operators—folding and reheating—drawing inspiration from the traditional Japanese swordsmithing technique. Within this new framework, folding is conceptualized as a compression of the search space, while reheating involves restarting the cooling process in the original SA method. These additions retain the computational simplicity of the SA method while enhancing its search capabilities. The algorithm's performance is evaluated using 28 benchmark functions, encompassing multimodal, unimodal, composite, and shifted functions. The results indicate that the new method outperforms the original SA and other well-known algorithms.