Ant Colony Optimization and Constraint ... | WHSmith Books
Ant Colony Optimization and Constraint Programming

Ant Colony Optimization and Constraint Programming

By: Christine Solnon (author)Hardback

Up to 2 WeeksUsually despatched within 2 weeks

Description

Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search approaches and metaheuristics, and shows how they can be integrated within constraint programming languages. The second part describes the ant colony optimization metaheuristic and illustrates its capabilities on different constraint satisfaction problems. The third part shows how the ant colony may be integrated within a constraint programming language, thus combining the expressive power of constraint programming languages, to describe problems in a declarative way, and the solving power of ant colony optimization to efficiently solve these problems.

Contents

Foreword xi Acknowledgements xiii Chapter 1. Introduction 1 1.1. Overview of the book 2 Chapter 2. Computational Complexity 7 2.1. Complexity of an algorithm 8 2.2. Complexity of a problem 10 2.3. Where the most difficult instances can be found 15 2.4. Solving NP-hard problems in practice 21 PART I. CONSTRAINT PROGRAMMING 27 Introduction to Part I 29 Chapter 3. Constraint Satisfaction Problems 31 3.1. What is a constraint? 31 3.2. What is a constraint satisfaction problem? 33 3.3. Optimization problems related to CSPs 35 3.4. The n-queens problem 37 3.5. The stable marriage problem 43 3.6. Randomly generated binary CSPs 46 3.7. The car sequencing problem 47 3.8. Discussion 50 Chapter 4. Exact Approaches 53 4.1. Construction of a search tree 53 4.2. Constraint propagation 57 4.3. Ordering heuristics 60 4.4. From satisfaction to optimization problems 63 4.5. Discussion 65 Chapter 5. Perturbative Heuristic Approaches 69 5.1. Genetic algorithms 70 5.2. Local search 73 5.3. Particle swarm optimization 78 5.4. Discussion 80 Chapter 6. Constructive Heuristic Approaches 85 6.1. Greedy randomized approaches 86 6.2. Estimation of distribution algorithms 88 6.3. Ant colony optimization 90 6.4. Discussion 91 Chapter 7. Constraint Programming Languages 93 7.1. Constraint logic programming 94 7.2. Constraint programming libraries 96 7.3. Constraint-based local search 96 7.4. Discussion 99 PART II. ANT COLONY OPTIMIZATION 101 Introduction to Part II 103 Chapter 8. From Swarm Intelligence to Ant Colony Optimization 105 8.1. Complex systems and swarm intelligence 106 8.2. Searching for shortest paths by ant colonies 108 8.3. Ant system and the traveling salesman problem 111 8.4. Generic ACO framework 116 Chapter 9. Intensification versus Diversification 125 9.1. ACO mechanisms for intensifying the search 125 9.2. ACO mechanisms for diversifying the search 127 9.3. Balancing intensification and diversification 128 9.4. Measures of diversification/intensification 135 Chapter 10. Beyond Static Combinatorial Problems 141 10.1. Multi-objective problems 141 10.2. Dynamic optimization problems 145 10.3. Optimization problems over continuous domains 147 Chapter 11. Implementation Issues 151 11.1. Data structures 151 11.2. Selection of a component with respect to probabilities 154 11.3. Implementation of a local search procedure 157 11.4. Computation of diversification/intensification measures 157 PART III. CP WITH ACO 161 Introduction to Part III 163 Chapter 12. Sequencing Cars with ACO 165 12.1. Notation 165 12.2. A first pheromone structure for identifying good car sequences 166 12.3. A second pheromone structure for identifying critical cars 171 12.4. Combining the two pheromone structures 173 12.5. Comparison of the different ACO algorithms 174 12.6. Comparison of ACO with state-of-the-art approaches 178 12.7. Discussion 182 Chapter 13. Subset Selection with ACO 185 13.1. Subset selection problems 186 13.2. Description of Ant-SSP 189 13.3. Instantiations of Ant-SSP with respect to two pheromone strategies 192 13.4. Instantiation of Ant-SSP to solve CSPs 196 13.5. Experimental results 197 13.6. Discussion 202 Chapter 14. Integration of ACO in a CP Language 205 14.1. Framework for integrating ACO within a CP library 206 14.2. Illustration of ACO-CP on the car sequencing problem 210 14.3. Discussion 214 Chapter 15. Conclusion 215 15.1. Towards constraint-based ACO search 215 15.2. Towards a reactive ACO search 216 Bibliography 219 Index 231

Product Details

  • ISBN13: 9781848211308
  • Format: Hardback
  • Number Of Pages: 246
  • ID: 9781848211308
  • weight: 506
  • ISBN10: 1848211309

Delivery Information

  • Saver Delivery: Yes
  • 1st Class Delivery: Yes
  • Courier Delivery: Yes
  • Store Delivery: Yes

Prices are for internet purchases only. Prices and availability in WHSmith Stores may vary significantly

Close