Iterated Local Search (ILS) is a heuristic optimization algorithm designed to enhance the performance of local search methods. It operates by repeatedly applying a local search algorithm to an initial solution, followed by perturbations that allow the search process to escape local optima. This iterative process is aimed at finding a better solution to optimization problems, especially in complex landscapes where traditional methods may fail.
The basic framework of ILS involves the following steps:
- Initialization: Start with an initial solution obtained through a constructive method or a random selection.
- Local Search: Apply a local search algorithm to improve the current solution by exploring its neighborhood. The local search seeks to find a locally optimal solution.
- Perturbation: If the local search concludes at a local optimum, apply a perturbation mechanism to modify the current solution. This step is crucial as it helps the algorithm to jump out of local optima.
- Reinsertion: Use the perturbed solution as the new starting point and repeat the local search process.
This cycle continues until a stopping criterion is met, such as a maximum number of iterations or time limits. The strength of ILS lies in its balance between intensifying the search around promising areas of the solution space and diversifying the search to explore new regions. It is particularly effective for combinatorial optimization problems like the Traveling Salesman Problem (TSP) and job scheduling tasks.
ILS is widely used in various fields, including operations research, computer science, and artificial intelligence, due to its simplicity and effectiveness in finding high-quality solutions to complex problems.