Local search algorithms are widely applied to numerous complex computational problems, including problems in computer science (particularly artificial intelligence), mathematics, operations research, engineering, and bioinformatics. The genetic algorithm is a widely used strategy to solve problems such as the street vendor problem and the backpack problem. The backpack problem is an optimization problem that involves filling a bag with products to maximize the value of the bag. Formally, the task is to choose the items that we pack in the bag with a defined maximum weight that the bag can hold, so that the total value of the bag is maximum.
Below, we list several specific heuristics to improve local search, with the main objective of escaping local minimums. Since the local search depends on the initial solution, there is a high chance of being stuck in a local optimal. 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. 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.
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. Local search algorithms examine and analyze many solutions (search space), making local changes until an optimal solution is obtained or not.