APPLICATIONS OF SWARM INTELLIGENCE -- APPLICATIONS OF SWARM INTELLIGENCE -- CONTENTS -- PREFACE -- Chapter 1 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 -- Linguistic description on the effect of structural parameters of MOPSO on its search process -- 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 -- Chapter 2 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 -- Chapter 3 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 -- 5.2. The In-Core Fuel Management Optimization -- 5.2.1. A General Description of the ICFMO -- 5.2.2. Mathematical Formulation of the in-Core Fuel Management Optimization -- 5.2.2. Simulation of Angra 1 NPP with the Reactor Physics Code Recnod -- 5.2.2. PSORK Model for the ICFMO -- 6. Computational Experimental Results -- 6.1. Traveling Salesman Problem -- 6.2. In-Core Fuel Management Optimization -- 7. Discussion -- 7.1. Traveling Salesman Problem -- 7.2. In-Core Fuel Management Optimization -- 8. Conclusions -- Acknowledgments -- References -- Chapter 4 SWARM INTELLIGENCE AND ARTIFICIAL NEURAL NETWORKS -- Summary -- 1. Using Swarm Intelligence for Artificial Neural Networks Training and Structure Optimization -- 1.1. Artificial Neural Networks -- 1.2. Using Particle Swarm Optimization for Artificial Neural Networks Training and Structure Optimization.

1.3. Using Ant Colony Optimization for Artificial Neural Networks Training -- References -- Chapter5SWARMINTELLIGENCEFORTHESELF-ASSEMBLYOFNEURALNETWORKS -- Abstract -- 1.Introduction -- 2.Methods -- 2.1.Agents -- 2.2.Rules -- 2.3.Forces -- 2.3.1.ForcesGoverningCollective("Swarm")Movements -- 2.3.2.EnvironmentandRule-BasedForces -- 2.4.ExperimentalMethods -- 2.4.1.ComputationalExperiments -- 2.4.2.ImplementationDetails -- 2.4.3.ConnectivityMeasures -- 3.Results -- 3.1.SomatosensoryCortexModel -- 3.2.VisualCortexModel -- 3.3.RobustnessExperiments -- 4.Discussion -- 5.ConclusionsandFutureWork -- Acknowledgment -- References -- Chapter 6 APPLICATION OF PARTICLE SWARM OPTIMIZATION METHOD TO INVERSE HEAT RADIATION PROBLEM -- Abstract -- 1. Introduction -- 2. Principle of Algorithm -- 2.1. Hybrid Genetic Algorithm (HGA) -- 2.2. Particle Swarm Optimization (PSO) -- 3. Mathematical Formulation -- 3.1. Physical Model -- 3.2. Direct Problem -- 4. Results and Discussion -- 4.1. Inverse Analysis Procedure -- 4.2. Estimation of Wall Emissivities (Case 1, 2) -- 4.3. Simultaneous Estimation of an Absorption and a Scattering Coefficients (Case 3, 4) -- 4.4. Simultaneous Estimation of Emissivities, Absorption & Scattering Coefficients (Case 5, 6) -- 5. Conclusions -- Acknowledgment -- References -- Chapter 7 ANT COLONY OPTIMIZATION FOR FUZZY SYSTEM PARAMETER OPTIMIZATION: FROM DISCRETE TO CONTINUOUS SPACE -- Abstract -- 1. Introduction -- 2. Fuzzy System Parameter Optimization in Discrete and Continuous Spaces -- 3. Discrete Aco for Fuzzy System Parameter Optimization in Discrete Spcace -- 3.1. Basic Concept of Discrete Ant Colony Optimization (ACO) -- 3.2. Discrete ACO for FS Parameter Optimization -- 4. Continuous ACO for FS Parameter Optimization in Continuous Space -- 5. Simulations -- 6. Conclusion -- References.

Chapter 8 PARTICLE SWARM OPTIMIZATION: A SURVEY -- Abstract -- 1. Introduction -- 2. Particle Swarm Optimization (PSO) -- 2.1. Conventional PSO (Original PSO) -- 2.2. Basic PSO -- 2.3. Parameter Adjustment -- 2.3.1. Acceleration Coefficients -- 2.3.2. Maximum and Minimum Velocity (vmax and vmin) -- 2.3.3. Inertia Weight (w) -- 2.3.4. Personal-Best and Global-Best -- 2.4. Neighbourhood Topology -- 3. PSO vs. GA -- 4. Conventional Weaknesses of PSO -- 5. Solutions and Proposed Modifications -- 6. Discussion and Conclusion -- References -- Chapter 9 APPLICATION OF PSO TO ELECTROMAGNETIC AND RADAR-RELATED PROBLEMS IN NON COOPERATIVE TARGET IDENTIFICATION -- Abstract -- 1. Introduction -- 2. PSO Applied to Direction of Arrival Estimation -- 2.1. Problem Formulation -- 2.2. Application of PSO to DOA Estimation -- 2.3. The Fitness Function and the Solution Space Limits -- 2.4. Performance Analysis -- 2.4.1. Convergence Study -- 2.4.2. Angular Errors, Fitness Errors and Number of Iterations -- 2.4.3. Robustness against Noise -- 2.4.4. Resolution -- 2.4.5. Dependence of the Incoming Angle -- 3. Complex Dielectric Constant Estimation by PSO -- 3.1. Context -- 3.2. Introduction -- 3.3. Problem Formulation -- 3.4. Results -- 4. Conclusion -- Formulas -- References -- Chapter 10 ANT COLONY OPTIMIZATION: A POWERFUL STRATEGY FOR BIOMARKER FEATURE SELECTION -- Abstract -- Introduction -- Conclusion -- Acknowledgments -- References -- Chapter 11 SWARM INTELLIGENCE BASED ANONYMOUS AUTHENTICATION PROTOCOL FOR DYNAMIC GROUP MANAGEMENT IN EHRM SYSTEM -- Abstract -- 1. Introduction -- 2. Model of the System -- 3. BXAAP (Boolean Expression Anonymous Authentication Protocol) -- BXAAP Protocol Model -- Registration -- Key Distribution -- Verification -- 4. Ant Colony Optimization -- 5. Ant Colony Optimization Based Boolean Function Minimization.

5.1. Model of an Ant System -- 6. ABXE Algorithm-Construction and Design -- 6.1. Pheromone Deposition of the Ant Agent -- 6.2. Assignment of Energy Value -- 6.3. Computation of Energy Value for a Large Number of Users in a Group -- 6.4. Algorithm: Ant Colony Optimized Boolean Expression Evolver -- 7. Experimental Results -- 8. Analysis -- 8.1. Security Analysis -- 8.2. Protocol Analysis -- 9. Comparison with Existing Group Rekeying Methods -- 9.1. Comparison of BXE Algorithm with ABXE Algorithm -- Conclusion -- Acknowledgment -- References -- INDEX -- Blank Page.