Adaptive multi-operator metaheuristics for quadratic assignment problems

Abstract

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. Tuning the parameters, i.e. the neighbourhood size and the perturbation rate, is essential for the performance of metaheuristics. We automatically adapt the parameters for the two restarting operators using variants of adaptive pursuit for the multi-operators metaheuristic algorithms. We experimentally study the performance of several instances of the new class of metaheuristics on the quadratic assignment problem (QAP) instances, a wellknown and difficult combinatorial optimization problem.