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Scatter Search Methodology and Implementations in C

Aus der Reihe Operations Research/Computer Science Interfaces Series
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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.02.2003

Abbildungen

XVII, 20 illus., schwarz-weiss Illustrationen

Verlag

Springer Us

Seitenzahl

291

Maße (L/B/H)

23,5/15,5/1,7 cm

Gewicht

476 g

Auflage

Softcover reprint of the original 1st ed. 2003

Sprache

Englisch

ISBN

978-1-4020-7376-2

Beschreibung

Rezension

From the reviews:



"The book Scatter Search by Manuel Laguna and Rafael Marti … provides an excellent introduction to this advanced optimization methodology. … Different from most other books in this field, this book comes along with a rich variety of illustrative examples for various optimization problems … . This significantly helps to gain an in-depth understanding of the methodology and enables readers to develop state-of-the-art implementations on their own. … With this book, the authors have created an excellent reference both for researchers and practitioners." (Stephan Scheuerer, OR-News, Issue 23, March, 2005)

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.02.2003

Abbildungen

XVII, 20 illus., schwarz-weiss Illustrationen

Verlag

Springer Us

Seitenzahl

291

Maße (L/B/H)

23,5/15,5/1,7 cm

Gewicht

476 g

Auflage

Softcover reprint of the original 1st ed. 2003

Sprache

Englisch

ISBN

978-1-4020-7376-2

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Scatter Search
  • Produktbild: Scatter Search
  • Foreword. Preface. Acknowledgements.
    1: Introduction. 1. Historical Background. 2. Basic Design. 3. C Code Conventions.
    2: Tutorial: Unconstrained Nonlinear Optimization. 1. Diversification Generation Method. 2. Improvement Method. 3. Reference Set Update Method. 4. Subset Generation Method. 5. Combination Method. 6. Overall Procedure. 7. Summary of C Functions.
    3: Tutorial: 0-1 Knapsack Problems. 1. Diversification Generation Method. 2. Improvement Method. 3. Reference Set Update Method. 4. Subset Generation Method. 5. Combination Method. 6. Overall Procedure. 7. Summary of C Functions.
    4: Tutorial: Linear Ordering Problem. 1. The Linear Ordering Problem. 2. Diversification Generation Method. 3. Improvement Method. 4. Reference Set Update Method. 5. Combination Method. 6. Summary of C Functions.
    5: Advanced Scatter Search Designs. 1. Reference Set. 2. Subset Generation. 3.Specialized Combination Methods. 4. Diversification Generation.
    6: Use of Memory in Scatter Search. 1. Tabu Search. 2. Explicit Memory. 3. Attributive Memory.
    7: Connections with Other Population-Based Approaches. 1. Genetic Algorithms. 2. Path Relinking. 3. Intensification and Diversification.
    8: Scatter Search Applications. 1. Neural Network Training. 2. Multi-Objective Bus Routing. 3. Arc Crossing Minimization in Graphs. 4. Maximum Clique. 5. Graph Coloring. 6. Periodic Vehicle Loading. 7. Capacitated Multicommodity Network Design. 8. Job-Shop Scheduling. 9. Capacitated Chinese Postman Problem. 10. Vehicle Routing. 11. Binary Mixed Integer Programming. 12. Iterated Re-start Procedures. 13. Parallelization for the P-Median. 14. OptQuest Application.
    9: Commercial Scatter Search Implementation. 1. General OCL Design. 2. Constraints and Requirements. 3. OCL Functionality. 4. Computational Experiments. 5. Conclusions. 6. Appendix.
    10: Experiences and Future Directions. 1. Experiences and Findings. 2. Multi-Objective Scatter Search. 3. Maximum Diversity Problem. 4. Implications for Future Developments.
    References. Index.