Hybrid metaheuristics: an emerging approach to optimization

Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as metaheuristics. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization. In recent years it has become evident that a skilled combination of a metaheuristic with other optimization techniques, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility. This is because hybrid metaheuristics combine their advantages with the complementary strengths of, for example, more classical optimization techniques such as branch and bound or dynamic programming. The authors involved in this book are among the top researchers in their domain. Presents recent developments in hybridmetaheuristics INDICE: Hybrid Metaheuristics: An Introduction.- Combining (Integer) Linear Programming Techniques and Metaheuristics for Combinatorial Optimization.- The Relation Between Complete and Incomplete Search.- Hybridizations of Metaheuristics With Branch and Bound Derivates.- Very Large-Scale Neighborhood Search: Overview and Case Studies on Coloring Problems.- Hybrids of Constructive Metaheuristics and Constraint Programming: A Case Study with ACO.- Hybrid Metaheuristics for Packing Problems.- Hybrid Metaheuristics for Multi-objective Combinatorial Optimization.- Multilevel Refinement for Combinatorial Optimisation: Boosting Metaheuristic Performance.

• ISBN: 978-3-540-78294-0
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 290
• Fecha Publicación: 01/03/2008
• Nº Volúmenes: 1
• Idioma: Inglés