The combination of components from different algorithms is currently one of the most successful trends in optimization. The hybridization of metaheuristics such as ant colony optimization, evolutionary algorithms, and variable neighborhood search with techniques from operations research and artificial intelligence plays hereby an important role. The resulting hybrid algorithms are generally labelled hybrid metaheuristics. The rising of this new research field was due to the fact that the focus of research in optimization has shifted from an algorithm-oriented point of view to a problem-oriented point of view. In this brief survey on hybrid metaheuristics we provide an overview on some of the most interesting and representative developments
