Hybrid Metaheuristics: Preface to the proceedings of HM2005

Combinatorial optimization attracted many researchers since more than three decades. Plenty of classical hard problems have been tackled successfully with metaheuristic approaches. Several thereof are currently considered state-of-the-art methods for such problems. However, for many years the main focus of research was on the application of single metaheuristics to given problems. A tendency to compare different metaheuristics against each other could be observed, and sometimes this competition led to thinking in stereotypes in the research communities. In recent years, it has become evident that the concentration on a sole metaheuristic is rather restrictive, when focusing on the improvement of heuristic techniques to tackle both academic and practical optimization problems. A skilled combination of concepts stemming from different metaheuristics can provide a more efficient behavior and a higher flexibility. Also the hybridization of metaheuristics with other techniques known from classical artificial intelligence areas can be very fruitful. Further, the incorporation of typical operations research techniques can be very beneficial. Combinations of metaheuristic components with components from other metaheuristics or optimization strategies from artificial intelligence or operations research are called hybrid metaheuristics. The design and implementation of hybrid metaheuristics rises problems going beyond questions about the composition of a single metaheuristic. The proper interaction of different algorithm components must usually be based on a careful analysis of the single components. Choice and tuning of parameters is more important for the quality of the algorithms than before. Different concepts of interaction at low-level and at high-level are studied. As a result, the design of experiments and the proper statistical evaluation are in a more exposed position than before. We believe that the combination of elements coming from different metaheuristics, and from classical methods from both artificial intelligence and operations research, bears great chances to become one of the main tracks of research in applied artificial intelligence. It seems to be a promising and rewarding alternative to the still existing mutual contempt between the fields of exact methods and approximate techniques, and also to the competition between the different schools of metaheuristics, which sometimes focused more on a proof of concept than on good general results. Still, we have to realize that research on hybrid metaheuristics is in main parts based on experimental methods, thus being probably more related to natural sciences than to computer science. It can be stated that both the design and the evaluation of experiments have still not reached the standard as they have in physics or chemistry for example. The validity of analyses of experimental work on algorithms is a key aspect in hybrid metaheuristics, and the attention of researchers to this aspect seems to be important for the future of the field.