Scatter Search

Methodology and Implementations in C

AvManuel Laguna,Rafael Martí

Häftad, Engelska, 2003

Del i serien Operations Research/Computer Science Interfaces Series

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Häftad

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Beskrivning

The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Contemporary studies demonstrate the practical advantages of this approach for solving a diverse array of optimization problems from both classical and real world settings. Scatter search contrasts with other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions in Euclidean space and by utilizing strategic designs where other approaches resort to randomization. The book's goal is to provide the basic principles and fundamental ideas that will allow the readers to create successful applications of scatter search. The book includes the C source code of the methods introduced in each chapter.

Produktinformation

Utgivningsdatum:2003-02-28
Mått:155 x 235 x 17 mm
Vikt:476 g
Format:Häftad
Språk:Engelska
Serie:Operations Research/Computer Science Interfaces Series
Antal sidor:291
Förlag:Kluwer Academic Publishers
ISBN:9781402073762

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Recensioner i media

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)

Innehållsförteckning

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.