| Titre : | Ant colony optimization |
| Auteurs : | Marco Dorigo, Auteur ; Thomas Stützle, Auteur |
| Type de document : | Monographie imprimée |
| Editeur : | Cambridge, Mass. : The MIT Press, cop. 2004 |
| ISBN/ISSN/EAN : | 978-0-262-04219-2 |
| Format : | 1 vol. (XIV-305 p.) / graph., ill., couv. ill. en coul. / 24 cm |
| Langues: | Anglais |
| Index. décimale : | 519.3 |
| Catégories : |
[Agneaux] Algorithmes optimaux [Agneaux] Fourmis > Moeurs et comportement > Modèles mathématiques [Agneaux] Optimisation mathématique |
| Résumé : |
The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.
The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms. |
| Sommaire : |
Preface ix
Acknowledgments xiii 1 From Real to Artificial Ants 1 1.1 Ants’ Foraging Behavior and Optimization 1 1.2 Toward Artificial Ants 7 1.3 Artificial Ants and Minimum Cost Paths 9 1.4 Bibliographical Remarks 21 1.5 Things to Remember 22 1.6 Thought and Computer Exercises 23 2 The Ant Colony Optimization Metaheuristic 25 2.1 Combinatorial Optimization 25 2.2 The ACO Metaheuristic 33 2.3 How Do I Apply ACO? 38 2.4 Other Metaheuristics 46 2.5 Bibliographical Remarks 60 2.6 Things to Remember 61 2.7 Thought and Computer Exercises 63 3 Ant Colony Optimization Algorithms for the Traveling Salesman Problem 65 3.1 The Traveling Salesman Problem 65 3.2 ACO Algorithms for the TSP 67 3.3 Ant System and Its Direct Successors 69 3.4 Extensions of Ant System 76 3.5 Parallel Implementations 82 3.6 Experimental Evaluation 84 3.7 ACO Plus Local Search 92 3.8 Implementing ACO Algorithms 99 3.9 Bibliographical Remarks 114 3.10 Things to Remember 117 3.11 Computer Exercises 117 4 Ant Colony Optimization Theory 121 4.1 Theoretical Considerations on ACO 121 4.2 The Problem and the Algorithm 123 4.3 Convergence Proofs 127 4.4 ACO and Model-Based Search 138 4.5 Bibliographical Remarks 149 4.6 Things to Remember 150 4.7 Thought and Computer Exercises 151 5 Ant Colony Optimization for N P-Hard Problems 153 5.1 Routing Problems 153 5.2 Assignment Problems 159 5.3 Scheduling Problems 167 5.4 Subset Problems 181 5.5 Application of ACO to Other N P-Hard Problems 190 5.6 Machine Learning Problems 204 5.7 Application Principles of ACO 211 5.8 Bibliographical Remarks 219 5.9 Things to Remember 220 5.10 Computer Exercises 221 6 AntNet: An ACO Algorithm for Data Network Routing 223 6.1 The Routing Problem 223 6.2 The AntNet Algorithm 228 6.3 The Experimental Settings 238 6.4 Results 243 6.5 AntNet and Stigmergy 252 6.6 AntNet, Monte Carlo Simulation, and Reinforcement Learning 254 6.7 Bibliographical Remarks 257 6.8 Things to Remember 258 6.9 Computer Exercises 259 7 Conclusions and Prospects for the Future 261 7.1 What Do We Know about ACO? 261 7.2 Current Trends in ACO 263 7.3 Ant Algorithms 271 Appendix: Sources of Information about the ACO Field 275 References 277 Index |
Disponibilité (1)
| Cote | Support | Localisation | Statut | Emplacement | |
|---|---|---|---|---|---|
| SI8/3094 | Livre | BIB.FAC.ST. | Empruntable | Magazin |
Documents numériques (1)
 BOOK URL |
Erreur sur le template
