Applications of swarm intelligence /

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Walters, Louis P. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Nova Science Publishers, Inc., [2011]
Colección:Engineering tools, techniques and tables.
Temas:
Swarm intelligence.
Problem solving.
Problem Solving
Résolution de problème.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Problem solving
Swarm intelligence
Acceso en línea:Texto completo
Tabla de Contenidos:
  • ""APPLICATIONS OF SWARM INTELLIGENCE""; ""APPLICATIONS OF SWARM INTELLIGENCE""; ""CONTENTS ""; ""PREFACE ""; ""SWARM INTELLIGENCE AND FUZZY SYSTEMS ""; ""Abstract ""; ""1. Optimizing the Parameters of Fuzzy Systems Using Swarm Intelligence Algorithms ""; ""1.1. Fuzzy Systems ""; ""1.1.1. Membership Functions ""; ""1.1.2. Fuzzy Rules ""; ""1.2. Designing a Fuzzy Classifier Using Particle Swarm Optimization Algorithm (PSO) ""; ""1.2.1. Integer-Valued Particle Swarm Optimization with Constriction Coefficient ""; ""1.2.2. Particle Representation""; ""1.2.3. Fitness Function Definition ""
  • ""1.3. Experimental Results """"1.4. Other Related Researches ""; ""2- Intelligently Controlling the Multi-objective Swarm Intelligence Parameters Using Fuzzy Systems ""; ""2.1. A Review on the Past Researches on Multi-objective PSO ""; ""2.2. Fuzzy-MOPSO Algorithm ""; ""2.2.1. Integer-Valued MOPSO with Constriction Coefficient ""; ""2.2.2. Designing Fuzzy-Controller for MOPSO ""; ""2.2.2.1. Metrics of Performance""; ""a) Minimal spacing ""; ""b) Aggregation factor ""; ""2.2.2.2. Fuzzy Parameters ""; ""a) Inputs of fuzzy controller ""; ""b) Outputs of fuzzy controller ""; ""c) Fuzzy rules ""
  • ""2.3. Space Allocation (Problem Description and Formulation) """"2.4. Implementation and Experimental Results ""; ""2.4.1. Application on Well-Known Benchmarks ""; ""2.4.2. Application of Fuzzy-MOPSO on Space Allocation ""; ""a) Particle Representation ""; ""b) Experimental and Comparative Results ""; ""3. Conclusion ""; ""References ""; ""EVOLUTIONARY STRATEGIES TO FIND PARETO FRONTS IN MULTIOBJECTIVE PROBLEMS ""; ""Abstract ""; ""1. Introduction ""; ""2. Pareto Optimality ""; ""3. Multi-objective Optimization with PSO""; ""A1. Algorithm for MOPSO ""; ""4. Movement Strategies ""
  • ""4.1. Ms1: Pick a Global Guidance Located in the Least Crowded Areas """"A2. Algorithm for Ms1 ""; ""4.2. Ms2: Create the Perturbation with Differential Evolution Concept ""; ""A3. Algorithm for Ms2 ""; ""4.3. Ms3: Search the Unexplored Space in the Non-Dominated Front ""; ""A4. Algorithm for Ms3 ""; ""4.4. Ms4: Combination of Ms1 and Ms2 ""; ""4.5. Ms5: Explore Solution Space with Mixed Particles ""; ""4.6. Ms6: Adaptive Weight Approach ""; ""5. Design of Experiments ""; ""6. Results and Discussions ""; ""7. Conclusions ""; ""Acknowledgment ""; ""References ""
  • ""PARTICLE SWARM OPTIMIZATION APPLIED TO REAL-WORLD COMBINATORIAL PROBLEMS: THE CASE STUDY OF THE IN-CORE FUEL MANAGEMENT OPTIMIZATION """"Abstract ""; ""1. Introduction ""; ""2. Particle Swarm Optimization ""; ""3. Models of Particle Swarm Optimization for Combinatorial Problems ""; ""4. Particle Swarm Optimization with Random Keys ""; ""4.1. Random Keys ""; ""4.2. Particle Swarm Optimization with Random Keys ""; ""5. Optimization of Real-World Problems: The Case Study of the in-Core Fuel Management Optimization ""; ""5.1. The Traveling Salesman Problem ""

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