The chapter introduces the concept of Iterated Local Search (ILS) as a metaheuristic for solving combinatorial optimization problems. ILS is designed to improve upon the performance of local search algorithms by iteratively applying perturbations to generate new solutions, leading to better solutions than random restart approaches. The key idea is to use a black-box heuristic (local search) and iteratively apply perturbations to explore the solution space more effectively. The chapter discusses the importance of choosing appropriate perturbations and acceptance criteria, and provides examples of their impact on the performance of ILS. It also highlights the role of initial solutions and the interaction between different components of ILS. The chapter concludes with a discussion on optimizing ILS for specific problems, emphasizing the need to consider problem-specific characteristics and the trade-offs between intensification and diversification.The chapter introduces the concept of Iterated Local Search (ILS) as a metaheuristic for solving combinatorial optimization problems. ILS is designed to improve upon the performance of local search algorithms by iteratively applying perturbations to generate new solutions, leading to better solutions than random restart approaches. The key idea is to use a black-box heuristic (local search) and iteratively apply perturbations to explore the solution space more effectively. The chapter discusses the importance of choosing appropriate perturbations and acceptance criteria, and provides examples of their impact on the performance of ILS. It also highlights the role of initial solutions and the interaction between different components of ILS. The chapter concludes with a discussion on optimizing ILS for specific problems, emphasizing the need to consider problem-specific characteristics and the trade-offs between intensification and diversification.