👉 Salt computing is an innovative approach to solving complex optimization problems, particularly those involving large datasets and non-linear constraints. It borrows concepts from nature, such as the way halite crystals (rock salt) form in brine solutions, to create a parallel processing system. In this method, multiple candidate solutions are generated simultaneously and evaluated using a "salt" function, which is designed to mimic the properties of halite's crystal lattice. The "salt" function assigns a score to each candidate solution based on how well it approximates the optimal solution. These scores are then used to guide a search process, where promising candidates are amplified and combined through a process called "salting," while less promising ones are discarded. This iterative refinement continues until a satisfactory solution is found or computational resources are exhausted, making salt computing particularly effective for problems like financial modeling, logistics optimization, and machine learning parameter tuning.
