Iterated Local Search (ILS) ist eine Heuristik Optimierungsalgorithmus designed to enhance the performance of local search methods. It operates by repeatedly applying a lokaler Suchalgorithmus to an initial solution, followed by perturbations that allow the search process to escape local optima. This Iterativer Prozess is aimed at finding a better solution to optimization problems, especially in complex landscapes where traditional methods may fail.
Das grundlegende Rahmenwerk von ILS umfasst die folgenden Schritte:
- Initialisierung: Start with an initial solution obtained through a constructive method or a random selection.
- Lokale Suche: Apply a local search algorithm to improve the current solution by exploring its neighborhood. The local search seeks to find a locally optimale Lösung.
- Störung: If the local search concludes at a lokalen Optimum nähert, apply a perturbation mechanism to modify the current solution. This step is crucial as it helps the algorithm to jump out of local optima.
- Wiedereinfügung: Verwendung 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 kombinatorische Optimierung Probleme wie das Traveling Salesman Problem (TSP) und Aufgaben der Jobplanung.
ILS is widely used in various fields, including operations research, computer science, and künstliche Intelligenz, due to its simplicity and effectiveness in finding high-quality solutions to complex problems.