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Mathematics | Scatter Search - Methodology and Implementations in C

Scatter Search

Methodology and Implementations in C

Laguna, Manuel, Martí, Rafael

Softcover reprint of the original 1st ed. 2003, XVII, 291 p.

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The book Scatter Search by Manuel Laguna and Rafael Mart! 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.

Content Level » Research

Keywords » algorithms - code - evolution - genetic algorithms - knowledge - linear optimization - memory - nonlinear optimization - optimization - problem solving - programming - scheduling

Related subjects » Applications - Artificial Intelligence - Mathematics

Table of contents 

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.

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