Scatter Search: Methodology and Implementations in C

ISBN-13: 9781461350279 / Angielski / Miękka / 2012 / 291 str.

Manuel Laguna; Rafael Marti

Opis

The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Recent 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.

From the Foreword:
Scatter Search represents a "missing link" in the literature of evolutionary methods... From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" or ("memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s. Yet this theme is an integral part of the scatter search methodology proposed a decade earlier, and the form and scope of such heuristic strategies embedded in scatter search continue to set it apart. Although there are points in common between scatter search and other evolutionary approaches, principally as a result of changes that have brought other approaches closer to scatter search in recent years, there remain differences that have an important impact on practical outcomes. Reflecting this impact, a hallmark of the present book is its focus on practical problem solving. Laguna and Marti give the reader the tools to create scatter search implementations for problems from a wide range of settings. Although theoretical problems (such as abstract problems in graph theory) are included, beyond a doubt the practical realm has a predominant role in this book....'
Fred Glover, University of Colorado

Recenzje

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)

Spis treści

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.

Blurb

The book Scatter Search by Manuel Laguna and Rafael Martí represents a long-awaited "missing link" in the literature of evolutionary methods. Scatter Search (SS)-together with its generalized form called Path Relinking-constitutes the only evolutionary approach that embraces a collection of principles from Tabu Search (TS), an approach popularly regarded to be divorced from evolutionary procedures. The TS perspective, which is responsible for introducing adaptive memory strategies into the metaheuristic literature (at purposeful level beyond simple inheritance mechanisms), may at first seem to be at odds with population-based approaches. Yet this perspective equips SS with a remarkably effective foundation for solving a wide range of practical problems. The successes documented by Scatter Search come not so much from the adoption of adaptive memory in the range of ways proposed in Tabu Search (except where, as often happens, SS is advantageously coupled with TS), but from the use of strategic ideas initially proposed for exploiting adaptive memory, which blend harmoniously with the structure of Scatter Search. From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" (or "memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s.