Iterated Local Search (ILS) é uma heurística algoritmo de otimização designed to enhance the performance of local search methods. It operates by repeatedly applying a algoritmo de busca local to an initial solution, followed by perturbations that allow the search process to escape local optima. This processo iterativo is aimed at finding a better solution to optimization problems, especially in complex landscapes where traditional methods may fail.
A estrutura básica do ILS envolve os seguintes passos:
- Inicialização: Start with an initial solution obtained through a constructive method or a random selection.
- Busca Local: Apply a local search algorithm to improve the current solution by exploring its neighborhood. The local search seeks to find a locally solução ótima.
- Perturbação: If the local search concludes at a ótimo 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.
- Reinserção: Usar 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 otimização combinatória problemas como o Problema do Caixeiro Viajante (TSP) e tarefas de escalonamento de trabalho.
ILS is widely used in various fields, including operations research, computer science, and inteligência artificial, due to its simplicity and effectiveness in finding high-quality solutions to complex problems.