Adaptive multi-operator metaheuristics for quadratic assignment problems

Local search based algorithms are a general and computational efficient metaheuristic. Restarting strategies are used in order to not be stuck in a local optimum. Iterated local search restarts the local search using perturbator operators, and the variable neighbourhood search alternates local search with various neighbourhoods. These two popular restarting techniques, or operators, evolve independently and are disconnected. We propose a metaheuristic framework, we call it multi-operator metaheuristics, which allows the alternative or simultaneously usage of the two restarting methods.