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Table of Contents
Preface; Contents; 1 Introduction; 1.1 Soft Computing; 1.2 Fuzzy Logic; 1.3 Neural Networks; 1.4 Evolutionary Computation; 1.5 Definition of an Optimization Problem; 1.6 Classical Optimization; 1.7 Optimization with Evolutionary Computation; 1.8 Soft Computing in Engineering; References; 2 Motion Estimation Algorithm Using Block-Matching and Harmony Search Optimization; 2.1 Introduction; 2.2 Harmony Search Algorithm; 2.2.1 The Harmony Search Algorithm; 2.2.1.1 Initializing the Problem and Algorithm Parameters; 2.2.1.2 Harmony Memory Initialization; 2.2.1.3 Improvisation of New Harmony Vectors
2.2.1.4 Updating the Harmony Memory2.2.2 Computational Procedure; 2.3 Fitness Approximation Method; 2.3.1 Updating the Individual Database; 2.3.2 Fitness Calculation Strategy; 2.3.3 HS Optimization Method; 2.4 Motion Estimation and Block-Matching; 2.5 Block-Matching Algorithm Based on Harmony Search with the Estimation Strategy; 2.5.1 Initial Population; 2.5.2 Tuning of the HS Algorithm; 2.5.3 The HS-BM Algorithm; 2.5.4 Discussion on the Accuracy of the Fitness Approximation Strategy; 2.6 Experimental Results; 2.6.1 HS-BM Results; 2.6.2 Results on H.264; 2.7 Conclusions; References
3 Gravitational Search Algorithm Applied to Parameter Identification for Induction Motors3.1 Introduction; 3.2 Problem Statement; 3.3 Gravitational Search Algorithm; 3.4 Experimental Results; 3.4.1 Induction Motor Parameter Identification; 3.4.2 Statistical Analysis; 3.5 Conclusions; References; 4 Color Segmentation Using LVQ Neural Networks; 4.1 Introduction; 4.1.1 Histogram Thresholding and Color Space Clustering; 4.1.2 Edge Detection; 4.1.3 Probabilistic Methods; 4.1.4 Soft-Computing Techniques; 4.1.5 Scheme; 4.2 Background Issues; 4.2.1 RGB Space Color; 4.2.2 Artificial Neural Networks
4.3 Competitive Networks4.4 Learning Vectors Quantization Vectors; 4.5 Architecture of the Color Segmentation System; 4.6 Implementation; 4.7 Results and Discussion; 4.8 Conclusions; References; 5 Global Optimization Using Opposition-Based Electromagnetism-Like Algorithm; 5.1 Introduction; 5.2 Electromagnetism: Like Optimization Algorithm (EMO); 5.2.1 Initialization; 5.2.2 Local Search; 5.2.3 Total Force Vector Computation; 5.2.4 Movement; 5.3 Opposition-Based Learning (OBL); 5.3.1 Opposite Number; 5.3.2 Opposite Point; 5.3.3 Opposite-Based Optimization
5.4 Opposition-Based Electromagnetism-Like Optimization Algorithm5.4.1 Opposition-Based Population Initialization; 5.4.2 Opposition-Based Production for New Generation; 5.5 Experimental Results; 5.5.1 Test Problems; 5.5.2 Parameter Settings for the Involved EMO Algorithms; 5.5.3 Results; 5.6 Conclusions; References; 6 Multi-threshold Segmentation Using Learning Automata; 6.1 Introduction; 6.2 Gaussian Approximation; 6.3 Learning Automata (LA); 6.3.1 CARLA Algorithm; 6.4 Implementation; 6.5 Experimental Results; 6.5.1 LA Algorithm Performance in Image Segmentation
2.2.1.4 Updating the Harmony Memory2.2.2 Computational Procedure; 2.3 Fitness Approximation Method; 2.3.1 Updating the Individual Database; 2.3.2 Fitness Calculation Strategy; 2.3.3 HS Optimization Method; 2.4 Motion Estimation and Block-Matching; 2.5 Block-Matching Algorithm Based on Harmony Search with the Estimation Strategy; 2.5.1 Initial Population; 2.5.2 Tuning of the HS Algorithm; 2.5.3 The HS-BM Algorithm; 2.5.4 Discussion on the Accuracy of the Fitness Approximation Strategy; 2.6 Experimental Results; 2.6.1 HS-BM Results; 2.6.2 Results on H.264; 2.7 Conclusions; References
3 Gravitational Search Algorithm Applied to Parameter Identification for Induction Motors3.1 Introduction; 3.2 Problem Statement; 3.3 Gravitational Search Algorithm; 3.4 Experimental Results; 3.4.1 Induction Motor Parameter Identification; 3.4.2 Statistical Analysis; 3.5 Conclusions; References; 4 Color Segmentation Using LVQ Neural Networks; 4.1 Introduction; 4.1.1 Histogram Thresholding and Color Space Clustering; 4.1.2 Edge Detection; 4.1.3 Probabilistic Methods; 4.1.4 Soft-Computing Techniques; 4.1.5 Scheme; 4.2 Background Issues; 4.2.1 RGB Space Color; 4.2.2 Artificial Neural Networks
4.3 Competitive Networks4.4 Learning Vectors Quantization Vectors; 4.5 Architecture of the Color Segmentation System; 4.6 Implementation; 4.7 Results and Discussion; 4.8 Conclusions; References; 5 Global Optimization Using Opposition-Based Electromagnetism-Like Algorithm; 5.1 Introduction; 5.2 Electromagnetism: Like Optimization Algorithm (EMO); 5.2.1 Initialization; 5.2.2 Local Search; 5.2.3 Total Force Vector Computation; 5.2.4 Movement; 5.3 Opposition-Based Learning (OBL); 5.3.1 Opposite Number; 5.3.2 Opposite Point; 5.3.3 Opposite-Based Optimization
5.4 Opposition-Based Electromagnetism-Like Optimization Algorithm5.4.1 Opposition-Based Population Initialization; 5.4.2 Opposition-Based Production for New Generation; 5.5 Experimental Results; 5.5.1 Test Problems; 5.5.2 Parameter Settings for the Involved EMO Algorithms; 5.5.3 Results; 5.6 Conclusions; References; 6 Multi-threshold Segmentation Using Learning Automata; 6.1 Introduction; 6.2 Gaussian Approximation; 6.3 Learning Automata (LA); 6.3.1 CARLA Algorithm; 6.4 Implementation; 6.5 Experimental Results; 6.5.1 LA Algorithm Performance in Image Segmentation