Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.
Author(s): Cesar Rego and Bahrain Alidaee
Edition: 1
Year: 2005
Language: English
Pages: 472
1402081340......Page 1
Contents......Page 6
Foreword......Page 8
1. A Scatter Search Tutorial for Graph-Based Permutation Problems......Page 15
2. A Multistart Scatter Search Heuristic for Smooth NLP and MINLP Problems......Page 39
3. Scatter Search Methods for the Covering Tour Problem......Page 72
4. Solution of the Sonet Ring Assignment Problem with Capacity Constraints......Page 105
5. A Very Fast Tabu Search Algorithm for Job Shop Problem......Page 129
6. Tabu Search Heuristics for the Vehicle Routing Problem......Page 157
7. Some New Ideas in TS for Job Shop Scheduling......Page 176
8. A Tabu Search Heuristic for the Uncapacitated Facility Location Problem......Page 202
9. Adaptive Memory Search Guidance for Satisfiability Problems......Page 223
10. Lessons from Applying and Experimenting with Scatter Search......Page 238
11. Tabu Search for Mixed-Integer Programming......Page 256
12. Scatter Search vs. Genetic Algorithms: An Experimental Evaluation with Permutation Problems......Page 271
13. Parallel Computation, Co-operation, Tabu Search......Page 291
14. Using Group Theory to Construct and Characterize Metaheuristic Search Neighborhoods......Page 311
15. Logistics Management: An Opportunity for Metaheuristics......Page 337
16. On the Integration of Metaheuristic Strategies in Constraint Programming......Page 365
17. General Purpose Metrics for Solution Variety......Page 380
18. Controlled Pool Maintenance for Metaheuristics......Page 393
19. Adaptive Memory Projection Methods for Integer Programming......Page 431
20. RAMP: A New Metaheuristic Framework for Combinatorial Optimization......Page 447
D......Page 467
I......Page 468
M......Page 469
P......Page 470
S......Page 471
W......Page 472
