La recherche locale itérée (ILS) est une heuristique algorithme d'optimisation designed to enhance the performance of local search methods. It operates by repeatedly applying a algorithme de recherche locale to an initial solution, followed by perturbations that allow the search process to escape local optima. This processus itératif is aimed at finding a better solution to optimization problems, especially in complex landscapes where traditional methods may fail.
Le cadre de base de l'ILS comprend les étapes suivantes :
- Initialisation : Start with an initial solution obtained through a constructive method or a random selection.
- Recherche Locale : Apply a local search algorithm to improve the current solution by exploring its neighborhood. The local search seeks to find a locally solution optimale.
- Perturbation : If the local search concludes at a optimum local, apply a perturbation mechanism to modify the current solution. This step is crucial as it helps the algorithm to jump out of local optima.
- Réinsertion : Utiliser 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 optimisation combinatoire problèmes tels que le problème du voyageur de commerce (TSP) et les tâches de planification de travail.
ILS is widely used in various fields, including operations research, computer science, and intelligence artificielle, due to its simplicity and effectiveness in finding high-quality solutions to complex problems.