Some examples of local search algorithms are WalkSat, the two-option algorithm for the street vendor problem, and the Metropolis-Hastings algorithm. A local search algorithm identifies the best route from an origin to the destination based on a randomly selected route. The local search algorithm explores and evaluates different solutions (search space) by applying local changes until an optimal solution is achieved or certain iterations are calculated. Improvements to the SLS can be made in the selection of the initial task and in the nature of the local changes considered, or by trying to escape local minimums.
Usually, moving from one state to the next involves only a local change in the value of a single variable, hence the name local search. Since the local search depends on the initial solution, there is a high chance of being stuck in a local optimal. Below, we list several specific heuristics to improve local search, with the main objective of escaping local minimums.