Local search algorithms

Local search algorithms are optimization techniques that iteratively improve a solution by exploring neighboring options. Starting from an initial solution, they make small changes to find better outcomes, aiming to reach a local optimum— the best solution nearby. These algorithms are useful for complex problems where exploring all possibilities is impractical. While they can quickly find good solutions, they might get stuck in local optima, missing the absolute best. Examples include hill climbing and simulated annealing, which balance exploration and refinement to efficiently navigate large solution spaces.