Inernational workshop on Hybrid metaheuristics - HM2004

Workshop on Hybrid metaheuristics, held as a satellite event of ECAI 2004 - Valencia, August 200

Hybrid Metaheuristics: Editorial to the special issue

Combinations of metaheuristic components with components from other metaheuristics or optimization strategies from AI and OR are called hybrid metaheuristics. The design and implementation of hybrid metaheuristics raises problems going beyond questions about the design of a single metaheuristic. Choice and tuning of parameters is for example rendered more difficult by the problem of how to achieve a proper interaction of different algorithm components. Interaction can take place at low-level, using functions from different metaheuristics, or at high-level, e.g., using a portfolio of metaheuristics for automated hybridization. This special issue of the Journal of Mathematical Modelling and Algorithms is devoted to this interdisciplinary topic and contains six papers covering a wide spectrum of subjects

Preface to Hybrid Metaheuristics - 7th International Workshop, HM 2010

Research in hybrid metaheuristics is now established as a reference field in the areas of optimization and problem solving. Hybrid metaheuristics have a strong impact on applications because they provide efficient and powerful problem solving techniques for optimization problems in industry. Furthermore, the related interdisciplinary research community provides a fertile environment where innovative techniques are presented and discussed. The International Workshop on Hybrid Metaheuristics pursues the direction of combining application-oriented and foundational research. This is demonstrated by the papers in the proceedings of this 7th HM event. The contributions selected for this volume represent an important sample of current research in hybrid metaheuristics. It is worth emphasizing that the selected papers cover both theoretical and applicational results, including applications to logistics and bioinformatics and new paradigmatic hybrid solvers

Proceedings of HM 2008 -- Fifth International Workshop on Hybrid Metaheuristics

The knowledge exploited to tackle difficult problems is probably the main theme of the papers selected for this fifth edition of the International Workshop on Hybrid Metaheuristics. Indeed, in most of the papers a specific combination of metaheuristics and other solving techniques is presented for tackling a particular relevant constrained optimization problem, such as fiber optic networks, timetabling and freight train scheduling problems. The quest for solvers which can successfully and efficiently handle relevant problems is the main motivation for research in metaheuristics: it is important to keep this in mind so as to clearly state our research goals and methodology. The question arises as to what is the definition of relevant problems and a possible answer is that any useful and even just interesting or funny problem can be considered as scientifically relevant. The research goal of solving relevant problems does not require practitioners to assemble some software code and, with a little faith in alchemy, hope that the outcome is a reasonably good solution. On the contrary, this research must be grounded on a scientific method and on technological skills. That is why it is so important to support the assessment of an algorithm’s performance with a sound methodology. This requires studying theoretical models for describing properties of the hybrid metaheuristics, and to be open to other communities and to compare our achievements with theirs

Preface to HM 2008 -- Fifth International Workshop on Hybrid Metaheuristics

The knowledge exploited to tackle difficult problems is probably the main theme of the papers selected for this fifth edition of the International Workshop on Hybrid Metaheuristics. Indeed, in most of the papers a specific combination of metaheuristics and other solving techniques is presented for tackling a particular relevant constrained optimization problem, such as fiber optic networks, timetabling and freight train scheduling problems. The quest for solvers which can successfully and efficiently handle relevant problems is the main motivation for research in metaheuristics: it is important to keep this in mind so as to clearly state our research goals and methodology. The question arises as to what is the definition of relevant problems and a possible answer is that any useful and even just interesting or funny problem can be considered as scientifically relevant. The research goal of solving relevant problems does not require practitioners to assemble some software code and, with a little faith in alchemy, hope that the outcome is a reasonably good solution. On the contrary, this research must be grounded on a scientific method and on technological skills. That is why it is so important to support the assessment of an algorithm\u2019s performance with a sound methodology. This requires studying theoretical models for describing properties of the hybrid metaheuristics, and to be open to other communities and to compare our achievements with theirs