Contents -- Preface -- Chapter 1 Swarm Intelligence and Neural Networks -- 1.1. Introduction -- 1.2. Swarm Intelligence -- 1.2.1. Particle Swarm Optimization -- 1.2.2. Ant Colony Optimization -- 1.2.3. Bee Colony Optimization -- 1.3. Neural Networks -- 1.3.1. Evolvable Neural Network -- 1.3.2. Higher Order Neural Network -- 1.3.3. Pi (Π)-Sigma (Σ) Neural Networks -- 1.3.4. Functional Link Artificial Neural Network -- 1.3.5. Ridge Polynomial Neural Networks (RPNNs) -- 1.4. Summary and Discussion -- References -- Chapter 2 Neural Network and Swarm Intelligence for Data Mining -- 2.1. Introduction -- 2.2. Testbeds for Data Mining -- 2.2.1. Fisher Iris Data -- 2.2.2. Pima - Diabetes Data -- 2.2.3. Shuttle Data -- 2.2.4. Classification Efficiency -- 2.3. Neural Network for Data Mining -- 2.3.1. Multi-Layer Perceptron (MLP) -- 2.3.2. Radial Basis Function Network -- 2.4. Swarm Intelligence for Data Mining -- 2.4.1. Ant Miner -- 2.4.2. Artificial Bee Colony -- 2.4.3. Particle Swarm Optimization -- 2.5. Comparative Study -- 2.6. Conclusions and Outlook -- Acknowledgments -- References -- Chapter 3 Multi-Objective Ant Colony Optimization: A Taxonomy and Review of Approaches -- 3.1. Introduction -- 3.2. Ant Colony Optimization -- 3.3. Basic Concepts of Multi-Objective Optimization -- 3.4. The ACO Metaheuristic for MOOPs in the Literature -- 3.5. ACO Variants for MOOP: A Refined Taxonomy -- 3.6. Promising Research Areas -- 3.7. Conclusions -- Acknowledgments -- References -- Chapter 4 Recurrent Neural Networks with Discontinuous Activation Functions for Convex Optimization -- 4.1. Introduction -- 4.2. Related Definitions and Lemmas -- 4.3. For Linear Programming -- 4.3.1. Model Description and Convergence Results -- 4.3.2. Simulation Results -- 4.4. For Quadratic Programming -- 4.4.1. Model Description -- 4.4.2. Convergence Results.

4.4.3. Simulation Results -- 4.5. For Non-Smooth Convex Optimization Subject to Linear Equality Constraints -- 4.5.1. Model Description and Convergence Results -- 4.5.2. Constrained Least Absolute Deviation -- 4.6. Application to k-Winners-Take-All -- 4.6.1. LP-Based Model -- 4.6.2. QP-Based Model -- 4.6.3. Simulation Results -- 4.7. Concluding Remarks -- Acknowledgments -- References -- Chapter 5 Automated Power Quality Disturbance Classification Using Evolvable Neural Network -- 5.1. Introduction -- 5.2. Wavelet Transform (WT) -- 5.3. Brief Overview of Neural Network Classifiers -- 5.4. Overview of Particle Swarm Optimization -- 5.5. Signal Generation, Feature Extraction and Classification -- 5.6. Results and Discussion -- 5.7. Conclusions -- References -- Chapter 6 Condition Monitoring and Fault Diagnosis Using Intelligent Techniques -- 6.1. Introduction -- 6.2. Methodology -- 6.2.1. Hardware Specification, System Setup and Audio Data Generation -- 6.2.2. Data Pre-Processing -- 6.2.3. Data Classification Techniques -- 6.2.4. Signal Segregation using Independent Component Analysis -- 6.3. Experimental Details -- 6.3.1. Pre-Processing -- 6.3.2. Method 1: Artificial Neural Network Setup for Engine Classification -- 6.3.3. Method 2: Artificial Neural Networks based PCA for feature extraction and ANN for classification -- 6.3.4. Method 3: Feature Extraction using Wigner Willey Transformation and Classification using Decision Trees -- 6.3.5. Method 4: NSUR and ICA based Classification -- 6.3.6. Method 5: ICA and FFT based Classification -- 6.4. Discussion and Conclusion -- References -- Chapter 7 Hue-Preserving Color Image Enhancement Using Particle Swarm Optimization -- 7.1. Introduction -- 7.2. Image Enhancement -- 7.2.1. Enhancement Function -- 7.2.2. Enhancement Evaluation Criterion -- 7.3. Proposed Methodology -- 7.3.1. Theory of PSO.

7.3.2. Proposed Methodology -- 7.3.3. Removal of Gamut Problem -- 7.3.4. Parameter Setting -- 7.4. Methods Compared With -- 7.4.1. Hue-Preserving Color Image Enhancement Without Gamut Problem (HPCIE) -- 7.4.2. A Genetic Algorithm Approach to Color Image Enhancement (GACIE) -- 7.5. Results and Discussion -- 7.6. Conclusion -- Acknowledgment -- References -- Chapter 8 Efficient Classifier Design with Hybrid Polynomial Neural Network -- 8.1. Introduction -- 8.2. Classification using PNN -- 8.2.1. PNN Architecture -- 8.2.2. Design Procedure of PNN Model -- 8.2.3. Experimental Studies with PNN -- 8.3. Particle Swarm Optimization -- 8.4. Classification with Reduced and Comprehensible Polynomial Neural Network -- 8.4.1. Experimental Studies with RCPNN -- 8.5. Conclusions -- References -- Chapter 9 Efficient Prediction of Retail Sales Using Differential Evolution Based Adaptive Model -- 9.1. Introduction -- 9.2. Adaptive Linear Combiner (ALC) -- 9.3. Basics of GA and DE Algorithms -- 9.3.1. Genetic Algorithm (GA) -- 9.3.2. Differential Evolution (DE) -- 9.4. Basics of PSO and BFO Algorithm -- 9.4.1. The Particle Swarm Optimization (PSO) -- 9.4.2. The Bacterial Foraging Optimization (BFO) -- 9.5. New Adaptive Models Using DE, GA, PSO and BFO Based Learning -- 9.5.1. Steps Involved in DE Based Training of the Model -- 9.5.2. Steps Involved in GA Based Training of the Model -- 9.5.3. Steps Involved in PSO Based Training of the Model -- 9.5.4. Steps Involved in BFO Based Training of the Model -- 9.6. Simulation Study -- 9.6.1. Experimental Data for Training and Testing -- 9.6.2. Training and Testing of the Forecasting Model -- 9.7. Conclusion -- References -- Chapter 10 Some Studies on Particle Swarm Optimization for Single and Multi-Objective Problems -- 10.1. Introduction -- 10.1.1. Definitions of Single and Multi-Objective Problem.

10.1.2. Particle Swarm Optimization (PSO) -- 10.2. Topological Structure of PSO -- 10.2.1. Multi-Objective Particle Swarm Optimization (MOPSO) -- 10.3. Comprehensive Review of PSO for Single Objective Optimization Problems -- 10.3.1. Discrete/Binary PSO -- 10.3.2. Adaptive PSO -- 10.3.3. Multi-Swarm PSO -- 10.3.4. Hybrid PSO -- 10.3.5. Other PSO -- 10.4. Comprehensive Review of Pareto-Based MOPSO Approaches -- 10.5. Applications with MOPSO Approaches -- 10.6. Issues and Sub-Issues of PSO and MOPSO -- 10.7. Summary and Future Scope -- References -- Chapter 11 Coherent Biclusters of Microarray Data by Imitating the Ecosystem: An Ant Colony Algorithmic Approach -- 11.1. Introduction to Microarray -- 11.1.1. Preparation of Microarrays -- 11.1.2. Design of Microarray or DNA Chip -- 11.1.3. How to Perform an Array Experiment -- 11.1.4. Importance of Gene Expression or Microarray Data -- 11.1.5. Applications of Gene Expression or Microarray Data -- 11.1.6. Advantages of Gene Expression or Microarray Data -- 11.2. Towards Biclustering of Microarray Data -- 11.2.1. Goal of Biclustering -- 11.2.2. Types of Bicluster -- 11.3. Bicluster Structure -- 11.3.1. Biclustering Algorithms -- 11.3.2. Applications of Biclustering -- 11.3.3. Procedures for Biclustering Analysis -- 11.3.4. Related Research -- 11.3.5. Goal -- 11.3.6. Problem Definition -- 11.3.7. Proposed Model -- 11.3.8. Ant colony optimization -- 11.3.9. Biclustering with Ant Colony Optimization -- 11.4. Conclusion -- References -- Author Index -- Subject Index.