Preface. I: Tabu Search. 1. Tabu Search Algorithms and Lower Bounds for the Resource-Constrained Project Scheduling Problem; T. Baar, et al. 2. Metaheuristic for the Vechile Routing Problem with Time Windows; J. Brandão. 3. New Heuristic Algorithms for the Crew Scheduling Problem; L. Cavique, et al. 4. Enhanced Continuous Tabu Search: An Algorithm for the Global Optimization of Multiminima Functions; R. Chelouah, P. Siarry. 5. Local Search in Constraint Programming: Experiments with Tabu Search on the Vehicle Routing Problem; B. de Backer, V. Furnon. 6. Tabu Search for Graph Coloring, T-Colorings and Set T-Colorings; R. Dorne, J.-K. Hao. 7. Tabu Search with Critical Event Memory: An Enhanced Application for Binary Quadratic Programs; F. Glover, et al. 8. Actuator Selection for the Control of Multi-Frequency Noise in Aircraft Interiors; R.K. Kincaid, S.L. Padula. 9. Neighborhood Search Algorithm for the Guillotine Non-Oriented Two-Dimensional Bin Packing Problem; A. Lodi, et al. 10. Candidate List and Exploration Strategies for Solving 0/1 MIP Problems Using a Pivot Neighborhood; A. Lokketangen, F. Glover. 11. Global and Local Moves in Tabu Search: A Real-Life Mail Collecting Application; R. Mechti, et al. 12. Flow Line Scheduling by Tabu Search; E. Nowicki, C. Smutnicki. Part II: Combined and Hybrid Approaches. 13. Using Lower Bounds in Minium Span Frequency Assignment; S.M. Allen, et al. 14. A Hybrid Heuristic for Multiobjective Knapsack Problems; F.B. Abdelaziz, et al. 15. Hybrid Genetic TabuSearch for a Cyclic Scheduling Problem; P. Greistorfer. Part III: Genetic and Evolutionary Algorithms. 16. Adaptive Genetic Algorithms: A Methodology for Dynamic Autoconfiguration of Genetic Search Algorithms; U. Derigs, et al. 17. The Lavish Ordering Genetic Algorithm; E. Falkenauer. 18. Fitness Landscapes and Performance of Meta-Heuristics; C. Fonlupt, et al. 19. A Network-Based Adaptive Evolutionary Algorithm for Constraint Satisfaction Problems; M.-C. Riff. Part IV: Ant Systems. 20. Applying the Ant System to the Vehicle Routing Problem; B. Bullnheimer, et al. 21. Cooperative Intelligent Search Using Adaptive Memory Techiques; L. Sondergeld, S. Voß. 22. The Max-Min Ant System and Local Search for Combinatorial Optimization Problems; T. Stützle, H. Hoos. Part V: Parallel Approaches. 23. Towards an Evolutionary Method &endash; Cooperating Multi-Thread Parallel Tabu Search Hybrid; T.G. Crainic, M. Gendreau. 24. Parallel Tabu Search for Large Optimization Problems; E.-G. Talbi, et al. 25. Sequential and Parallel Local Search Algorithms for Job Shop Scheduling; H.M.M. ten Eikelder, et al. 26. An Experimental Study of Systemic Behavior of Cooperative Search Algorithms; M. Toulouse, et al. Part VI: Further Meta-Heuristics. 27. A Hopfield-Tank Neural Network Model for the Generalized Traveling Salesman Problem; R. Andresol, et al. 28. Generalized Cybernetic Optimization: Solving Continuous Variable Problems; M.A. Fleischer. 29. Solving the Progressive Party Problem by Local Search; P.
Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.
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