Description based upon print version of record. 7.3.11 Cuckoo Search Algorithm with Wavelet Neural Network Model
Cover; Half Title; Series Page; Title Page; Copyright Page; Contents; Preface; Authors; Section I: Introduction to Heuristic Optimization; Chapter 1 Optimization Using Heuristic Search: An Introduction; 1.1 Introduction; 1.2 The Optimization Problem; 1.2.1 Local Versus Global Optima; 1.3 Categorization of Optimization Techniques; 1.4 Requirement of Heuristics and Their Characteristics; 1.5 Performance Measures for Heuristics; 1.6 Classification of Heuristics; 1.7 Conclusion; Section II: Description of Heuristic Optimization Techniques; Part I: Evolutionary Techniques Chapter 2 Genetic Algorithm2.1 Introduction; 2.2 Genetic Algorithm; 2.3 Competent Genetic Algorithm; 2.4 Improvements in Genetic Algorithms; 2.5 Conclusion; Chapter 3 Particle Swarm Optimization Algorithm; 3.1 Introduction; 3.2 Basics of Particle Swarm Optimization Approach; 3.2.1 Structure of Standard PSO; 3.2.2 Some Definitions; 3.3 PSO Algorithm; 3.4 Some Modified PSO Algorithms; 3.4.1 Quantum-Behaved PSO; 3.4.2 Chaotic PSO; 3.4.3 Time Varying Acceleration Coefficient-Based PSO; 3.4.4 Simpliefid PSO; 3.5 Benefits of PSO Algorithm; 3.6 Applications of PSO; 3.7 Conclusion Part II: Nature-Based TechniquesChapter 4 Ant Colony Optimization; 4.1 Introduction; 4.2 Components and Goals of ACO; 4.3 Traditional Approaches of ACO; 4.3.1 Ant System; 4.3.2 Max-Min Ant System; 4.3.3 Quantum Ant Colony Optimization; 4.3.4 Cooperative Genetic Ant System; 4.3.5 Cunning Ant System; 4.3.6 Model Induced Max-Min Ant System; 4.3.7 Ant Colony System; 4.4 Engineering Applications of Ant Colony Optimization Algorithm; 4.5 Conclusion; Chapter 5 Bees Algorithm; 5.1 Introduction; 5.2 Basic Version of Bees Algorithm; 5.3 Improvements on Bees Algorithm 5.3.1 Improvements Associated with Setting and Tuning of Parameters5.3.2 Improvements Considered on the Local and Global Search Phase; 5.3.3 Improvements Made in the Initialization of the Problem; 5.4 Conclusion; Chapter 6 Firefly Algorithm; 6.1 Introduction; 6.2 Biological Foundations; 6.3 Structure of Firefly Algorithm; 6.4 Characteristics of Firefly Algorithm; 6.5 Variants of Firefly Algorithm; 6.5.1 Modie Variants of Firefly Algorithm; 6.5.2 Hybrid Variants of Firefly Algorithm; 6.6 Engineering Applications of Firefly Algorithm; 6.7 Conclusion; Chapter 7 Cuckoo Search Algorithm 7.1 Introduction7.2 Cuckoo Search Methodology; 7.3 Variants of Cuckoo Search Algorithm; 7.3.1 Adaptive Cuckoo Search Algorithm; 7.3.2 Self-Adaptive Cuckoo Search Algorithm; 7.3.3 Cuckoo Search Clustering Algorithm; 7.3.4 Novel Adaptive Cuckoo Search Algorithm; 7.3.5 Cuckoo Search Algorithm Based on Self-Learning Criteria; 7.3.6 Discrete Cuckoo Search Algorithm; 7.3.7 Differential Evolution and Cuckoo Search Algorithm; 7.3.8 Cuckoo Inspired Fast Search; 7.3.9 Cuckoo Search Algorithm Integrated with Membrane Communication Mechanism; 7.3.10 Master-Leader-Slave Cuckoo
This book will cover heuristic optimization techniques and applications in engineering problems. The book will be divided into three sections that will provide coverage of the techniques, which can be employed by engineers, researchers, and manufacturing industries, to improve their productivity with the sole motive of socio-economic development. This will be the first book in the category of heuristic techniques with relevance to engineering problems and achieving optimal solutions. Features Explains the concept of optimization and the relevance of using heuristic techniques for optimal solutions in engineering problems Illustrates the various heuristics techniques Describes evolutionary heuristic techniques like genetic algorithm and particle swarm optimization Contains natural based techniques like ant colony optimization, bee algorithm, firefly optimization, and cuckoo search Offers sample problems and their optimization, using various heuristic techniques