Finding exact solutions to many combinatorial optimization problems in busi­ ness, engineering, and science still poses a real challenge, despite the impact of recent advances in mathematical programming and computer technology. New fields of applications, such as computational biology, electronic commerce, and supply chain management, bring new challenges and needs for algorithms and optimization techniques. Metaheuristics are master procedures that guide and modify the operations of subordinate heuristics, to produce improved approx­ imate solutions to hard optimization problems with respect to more simple algorithms. They also provide fast and robust tools, producing high-quality solutions in reasonable computation times. The field of metaheuristics has been fast evolving in recent years. Tech­ niques such as simulated annealing, tabu search, genetic algorithms, scatter search, greedy randomized adaptive search, variable neighborhood search, ant systems, and their hybrids are currently among the most efficient and robust optimization strategies to find high-quality solutions to many real-life optimiza­ tion problems. A very large nmnber of successful applications of metaheuristics are reported in the literature and spread throughout many books, journals, and conference proceedings. A series of international conferences entirely devoted to the theory, applications, and computational developments in metaheuristics has been attracting an increasing number of participants, from universities and the industry.

Inhalt

Preface. 1. Selected Topics in Simulated Annealing; E. Aarts, J. Korst. 2. Reactive Tabu Search with Path-Relinking for the Steiner Problem in Graphs; M.P. Bastos, C.C. Ribeiro. 3. A GRASP for Job Shop Scheduling; S. Binato, et al. 4. A Reactive GRASP for Transmission Network Expansion Planning; S. Binato, G.C. Oliveira. 5. Tabu Search for Two-Dimensional Irregular Cutting; J. Blazewicz, et al. 6. A Study of Global Convexity for a Multiple Objective Travelling Salesman Problem; P.C. Borgers, M.P. Hansen. 7. A Lower Bound Based Meta-Heuristic for the Vehicle Routing Problem; J. Brandão. 8. A Simulated Annealing Approach for Minimum Cost Isolated Failure Immune Networks; A. Candia, H. Bravo. 9. A GRASP Interactive Approach to the Vehicle Routing Problem with Backhauls; C. Carreto, B. Baker. 10. Parallel Cooperative Approaches for the Labor Constrained Scheduling Problem; C.C.B. Cavalcante, et al. 11. A Scatter Search Algorithm for the Maximum Clique Problem; L. Cavique, et al. 12. The Noising Methods: A Survey; I. Charon, O. Hudry. 13. Strategies for the Parallel Implementation of Metaheuristics; V.-D. Cung, et al. 14. Accelerating Strategies in Column Generation Methods for Vehicle Routing and Crew Scheduling Problems; G. Desaulniers, et al. 15. GRASP: An Annotated Bibliography; P. Festa, M.G.C. Resende. 16. Recent Advances in Tabu Search; M. Gendreau. 17. Lagrangean Tabu Search; T. Grünert. 18. A GIDS Metaheuristic Approach to the Fleet Size and Mix Vehicle RoutingProblem; A.F.-W. Han, Y.-J. Cho. 19. Developments of Variable Neighborhood Search; P. Hansen, N. Mladenović. 20. Analyzing the Performance of Local Search Algorithms Using Generalized Hill Climbing Algorithms; S.H. Jacobson. 21. Ant Colony Optimization: An Overview; V. Maniezzo, A. Carbonaro. 22. Intensification Neighborhoods for Local Search Methods; T. Mautor. 23. New Heuristics for the Euclidean Steiner Problem in Rn; F. Montenegro, et al. 24. Mathematical Analysis of Evolutionary Algorithms; H. Mühlenbein, Th. Mahnig. 25. Formulation and Tabu Search Algorithm for the Resource Constrained Project Scheduling Problem; K. Nonobe, T. Ibaraki. 26. Analysing the Run-Time Behaviour of Iterated Local Search for the Travelling Salesman Problem; T. Stützle, H.H. Hoos. 27. POPMUSIC &endash; Partial Optimization Metaheuristic under Special Intensification Conditions; E.D. Taillard, S. Voss. 28. Subcost-Guided Simulated Annealing; M. Wright. 29. A Pruning Pattern List Approach to the Permutation Flowshop Scheduling Problem; T. Yamada.