Iterated Local Search: Applications and Extensions

Iterated Local Search (ILS) is a conceptually simple and efficient well-known Metaheuristic. The main idea behind ILS is to drive the search not on the full space of all feasible solutions but on the solutions that are returned by some underlying algorithm; typically, local optimal solutions obtained by the application of a local search heuristic. This method has been applied to many different optimization problems having about 10,000 entries in Google Scholar. In this talk, we will review briefly the ILS method emphasizing the extensions of ILS. We will describe three relevant types of extensions: the hybrid ILS approaches combining ILS with other metaheuristics and/or exact methods; the SimILS (Simulation+ILS) to solve Stochastic Combinatorial Optimization Problems; the MoILS to solve Multiobjective Combinatorial Optimization, including multiobjective and stochastic problems. We will discuss the advantages and disadvantages of these extensions and present some applications, including real ones in areas like Marketing, Supply Chain Management, Logistics or Health Care.