Cargando…
Ant Colony Optimization and Constraint Programming.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Wiley,
2013.
|
| Colección: | ISTE.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Cpoyright Page; Table of Contents; Foreword; Acknowledgements; Chapter 1. Introduction; 1.1. Overview of the book; 1.1.1. Constraint programming; 1.1.2. Ant colony optimization; 1.1.3. Constraint programming with ant colony optimization; Chapter 2. Computational Complexity; 2.1. Complexity of an algorithm; 2.2. Complexity of a problem; 2.2.1. The P class; 2.2.2. The NP class; 2.2.3. NP-complete problems; 2.2.4. NP-hard problems; 2.2.5. Undecidable problems; 2.2.6. Complexity of optimization problems; 2.3. Where the most difficult instances can be found.
- 2.3.1. Phase transition2.3.2. Search landscape; 2.4. Solving NP-hard problems in practice; 2.4.1. Exploitation of particular cases; 2.4.2. Approximation algorithms; 2.4.3. Heuristics and metaheuristics; 2.4.4. Structuring and filtering the search space; PART I. CONSTRAINT PROGRAMMING; Introduction to Part I; Chapter 3. Constraint Satisfaction Problems; 3.1. What is a constraint?; 3.1.1. Definition of a constraint; 3.1.2. Arity of a constraint and global constraints; 3.2. What is a constraint satisfaction problem?; 3.2.1. Complexity of CSPs; 3.3. Optimization problems related to CSPs.
- 3.3.1. Maximizing constraint satisfaction3.3.2. Constrained optimization; 3.4. The n-queens problem; 3.4.1. Description of the problem; 3.4.2. First CSP model; 3.4.3. Second CSP model; 3.4.4. Third CSP model; 3.4.5. Influence of the model on the solution process; 3.5. The stable marriage problem; 3.5.1. Description of the problem; 3.5.2. CSP model; 3.6. Randomly generated binary CSPs; 3.7. The car sequencing problem; 3.7.1. Description of the problem; 3.7.2. CSP model; 3.8. Discussion; Chapter 4. Exact Approaches; 4.1. Construction of a search tree; 4.2. Constraint propagation.
- 4.2.1. Forward checking4.2.2. Maintaining arc consistency; 4.3. Ordering heuristics; 4.3.1. Heuristics for choosing variables; 4.3.2. Heuristics for choosing values; 4.3.3. Randomized restart; 4.4. From satisfaction to optimization problems; 4.5. Discussion; Chapter 5. Perturbative Heuristic Approaches; 5.1. Genetic algorithms; 5.1.1. Basic principles; 5.1.2. Using GAs to solve CSPs; 5.2. Local search; 5.2.1. Basic principles; 5.2.2. Metaheuristics based on LS; 5.2.3. Using LS to solve CSPs; 5.3. Particle swarm optimization; 5.3.1. Basic principles; 5.3.2. Using PSO to solve CSPs.
- 5.4. DiscussionChapter 6. Constructive Heuristic Approaches; 6.1. Greedy randomized approaches; 6.1.1. Basic principles; 6.1.2. Using greedy randomized algorithms to solve CSPs; 6.2. Estimation of distribution algorithms; 6.2.1. Basic principles; 6.2.2. Using EDAs to solve CSPs; 6.3. Ant colony optimization; 6.4. Discussion; Chapter 7. Constraint Programming Languages; 7.1. Constraint logic programming; 7.2. Constraint programming libraries; 7.3. Constraint-based local search; 7.4. Discussion; PART II. ANT COLONY OPTIMIZATION; Introduction to Part II.