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Optimization in Engineering Sciences : Approximate and Metaheuristic Methods.
The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Tec...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Contents; List of Figures; List of Tables; List of Algorithms; List of Acronyms; Preface; Acknowledgments; 1: Metaheuristics
- Local Methods; 1.1. Overview; 1.2. Monte Carlo principle; 1.3. Hill climbing; 1.4. Taboo search; 1.4.1. Principle; 1.4.2. Greedy descent algorithm; 1.4.3. Taboo search method; 1.4.4. Taboo list; 1.4.5. Taboo search algorithm; 1.4.6. Intensification and diversification; 1.4.7. Application examples; 1.4.7.1. Searching the smallest value on a table; 1.4.7.2. The problem of N queens; 1.5. Simulated annealing.
- 1.5.1. Principle of thermal annealing1.5.2. Kirkpatrick's model of thermal annealing; 1.5.3. Simulated annealing algorithm; 1.6. Tunneling; 1.6.1. Tunneling principle; 1.6.2. Types of tunneling; 1.6.2.1. Stochastic tunneling; 1.6.2.2. Tunneling with penalties; 1.6.3. Tunneling algorithm; 1.7. GRASP methods; 2: Metaheuristics
- Global Methods; 2.1. Principle of evolutionary metaheuristics; 2.2. Genetic algorithms; 2.2.1. Biology breviary; 2.2.2. Features of genetic algorithms; 2.2.2.1. Genetic operations; 2.2.2.2. Inheritors viability; 2.2.2.3. Selection for reproduction.
- 2.3.3. Hill climbing by group of alpinists2.4. Optimization by ant colonies; 2.4.1. Ant colonies; 2.4.1.1. Natural ants; 2.4.1.2. Aspects inspired from natural ants; 2.4.1.3. Features developed for the artificial ants; 2.4.2. Basic optimization algorithm by ant colonies; 2.4.3. Pheromone trail update; 2.4.3.1. Adaptive delayed update; 2.4.3.2. On-line update; 2.4.3.3. Update through elitist strategy; 2.4.3.4. Update by ants ranking; 2.4.4. Systemic ant colony algorithm; 2.4.5. Traveling salesman example; 2.5. Particle swarm optimization; 2.5.1. Basic metaheuristic; 2.5.1.1. Principle.
- 2.5.1.2. Particles dynamical model2.5.1.3. Selecting the informants; 2.5.2. Standard PSO algorithm; 2.5.3. Adaptive PSO algorithm with evolutionary strategy; 2.5.4. Fireflies algorithm; 2.5.4.1. Principle; 2.5.4.2. Dynamical model of fireflies behavior; 2.5.4.3. Standard fireflies algorithm; 2.5.5. Bats algorithm; 2.5.5.1. Principle; 2.5.5.2. Dynamical model of bats behavior; 2.5.5.3. Standard bats algorithm; 2.5.6. Bees algorithm; 2.5.6.1. Principle; 2.5.6.2. Dynamical and cooperative model of bees' behavior; 2.5.6.3. Standard bee algorithm; 2.5.7. Multivariable prediction by PSO.