Nature Inspired Optimization Algorithms 2nd edition by Xin She Yang – Ebook PDF Instant Download/Delivery: 0128219866 , 978-0128219867
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ISBN 10: 0128219866
ISBN 13: 978-0128219867
Author: Xin She Yang
Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book’s unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.
- Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
- Provides a theoretical understanding and practical implementation hints
- Presents a step-by-step introduction to each algorithm
- Includes four new chapters covering mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization algorithms, and the latest deep learning techniques, background and various applications
Nature Inspired Optimization Algorithms 2nd Table of contents:
Chapter 1 : Introduction to Algorithms
Abstract
Keywords
1.1 What Is an Algorithm?
1.2 Newton’s Method
1.3 Formulation of Optimization Problems
1.3.1 Optimization Formulation
1.3.2 Classification of Optimization Problems
1.3.3 Classification of Optimization Algorithms
1.4 Optimization Algorithms
1.4.1 Gradient-Based Algorithms
1.4.2 Hill Climbing With Random Restart
1.5 Search for Optimality
1.6 No-Free-Lunch Theorems
1.6.1 NFL Theorems
1.6.2 Choice of Algorithms
1.7 Nature-Inspired Metaheuristics
1.8 A Brief History of Metaheuristics
References
Chapter 2 : Mathematical Foundations
Abstract
Keywords
2.1 Introduction
2.2 Norms, Eigenvalues and Eigenvectors
2.2.1 Norms
2.2.2 Eigenvalues and Eigenvectors
2.2.3 Optimality Conditions
2.3 Sequences and Series
2.3.1 Convergence Sequences
2.3.2 Series
2.4 Computational Complexity
2.5 Convexity
2.6 Random Variables and Probability Distributions
2.6.1 Random Variables
2.6.2 Common Probability Distributions
2.6.3 Distributions With Long Tails
2.6.4 Entropy and Information Measures
References
Chapter 3 : Analysis of Algorithms
Abstract
Keywords
3.1 Introduction
3.2 Analysis of Optimization Algorithms
3.2.1 Algorithm as an Iterative Process
3.2.2 An Ideal Algorithm?
3.2.3 A Self-Organization System
3.2.4 Exploration and Exploitation
3.2.5 Evolutionary Operators
3.3 Nature-Inspired Algorithms
3.3.1 Simulated Annealing
3.3.2 Genetic Algorithms
3.3.3 Differential Evolution
3.3.4 Ant and Bee Algorithms
3.3.5 Particle Swarm Optimization
3.3.6 The Firefly Algorithm
3.3.7 Cuckoo Search
3.3.8 The Bat Algorithm
3.3.9 The Flower Algorithm
3.4 Other Algorithms and Recent Developments
3.5 Parameter Tuning and Parameter Control
3.5.1 Parameter Tuning
3.5.1.1 Hyperoptimization
3.5.1.2 Multi-Objective View
3.5.2 Parameter Control
3.6 Discussions
3.7 Summary
References
Chapter 4 : Random Walks and Optimization
Abstract
Keywords
4.1 Isotropic Random Walks
4.2 Lévy Distribution and Lévy Flights
4.3 Optimization as Markov Chains
4.3.1 Markov Chain
4.3.2 Optimization as a Markov Chain
4.4 Step Sizes and Search Efficiency
4.4.1 Step Sizes, Stopping Criteria, and Efficiency
4.4.2 Why Lévy Flights are more Efficient
4.5 Modality and Optimal Balance
4.5.1 Modality and Intermittent Search Strategy
4.5.2 Optimal Balance of Exploration and Exploitation
4.6 Importance of Randomization
4.6.1 Ways to Carry Out Random Walks
4.6.2 Importance of Initialization
4.6.3 Importance Sampling
4.6.4 Low-Discrepancy Sequences
4.7 Eagle Strategy
4.7.1 Basic Ideas of Eagle Strategy
4.7.2 Why Eagle Strategy is so Efficient
References
Chapter 5 : Simulated Annealing
Abstract
Keywords
5.1 Annealing and Boltzmann Distribution
5.2 SA Parameters
5.3 SA Algorithm
5.4 Basic Convergence Properties
5.5 SA Behavior in Practice
5.6 Stochastic Tunneling
References
Chapter 6 : Genetic Algorithms
Abstract
Keywords
6.1 Introduction
6.2 Genetic Algorithms
6.3 Role of Genetic Operators
6.4 Choice of Parameters
6.5 GA Variants
6.6 Schema Theorem
6.7 Convergence Analysis
References
Chapter 7 : Differential Evolution
Abstract
Keywords
7.1 Introduction
7.2 Differential Evolution
7.3 Variants
7.4 Choice of Parameters
7.5 Convergence Analysis
7.6 Implementation
References
Chapter 8 : Particle Swarm Optimization
Abstract
Keywords
8.1 Swarm Intelligence
8.2 PSO Algorithm
8.3 Accelerated PSO
8.4 Implementation
8.5 Convergence Analysis
8.5.1 Dynamical System
8.5.2 Markov Chain Approach
8.6 Binary PSO
References
Chapter 9 : Firefly Algorithms
Abstract
Keywords
9.1 The Firefly Algorithm
9.1.1 Firefly Behavior
9.1.2 Standard Firefly Algorithm
9.1.3 Variations of Light Intensity and Attractiveness
9.1.4 Controlling Randomization
9.2 Algorithm Analysis
9.2.1 Scalings and Limiting Cases
9.2.2 Attraction and Diffusion
9.2.3 Special Cases of FA
9.3 Implementation
9.4 Variants of the Firefly Algorithm
9.4.1 FA Variants
9.4.2 How Can We Discretize FA?
9.5 Firefly Algorithm in Applications
9.6 Why the Firefly Algorithm Is Efficient
References
Chapter 10 : Cuckoo Search
Abstract
Keywords
10.1 Cuckoo Breeding Behavior
10.2 Lévy Flights
10.3 Cuckoo Search
10.3.1 Special Cases of Cuckoo Search
10.3.2 How to Carry out Lévy Flights
10.3.3 Choice of Parameters
10.4 Implementation
10.5 Variants of Cuckoo Search
10.6 Why Cuckoo Search Is so Efficient
10.7 Global Convergence: Brief Mathematical Analysis
10.8 Applications
References
Chapter 11 : Bat Algorithms
Abstract
Keywords
11.1 Echolocation of Bats
11.1.1 Behavior of Microbats
11.1.2 Acoustics of Echolocation
11.2 Bat Algorithms
11.2.1 Movement of Virtual Bats
11.2.2 Loudness and Pulse Emission
11.3 Implementation
11.4 Binary Bat Algorithms
11.5 Variants of the Bat Algorithm
11.6 Convergence and Stability Analysis
11.7 Why the Bat Algorithm is Efficient
11.8 Applications
References
Chapter 12 : Flower Pollination Algorithms
Abstract
Keywords
12.1 Introduction
12.2 Flower Pollination Algorithm
12.2.1 Characteristics of Flower Pollination
12.2.2 Flower Pollination Algorithm
12.3 Implementation
12.4 Multi-Objective Flower Pollination Algorithm
12.5 Validation and Numerical Experiments
12.5.1 Single-Objective Test Functions
12.5.2 Multi-Objective Test Functions
12.5.3 Analysis of Results and Comparison
12.6 Engineering Design Benchmarks
12.6.1 Single-Objective Design Benchmarks
12.6.1.1 Spring Design Optimization
12.6.1.2 Welded Beam Design
12.6.1.3 Pressure Vessel Design
12.6.2 Bi-Objective Disc Design
12.7 Variants and Applications
References
Chapter 13 : A Framework for Self-Tuning Algorithms
Abstract
Keywords
13.1 Introduction
13.2 Algorithm Analysis and Parameter Tuning
13.2.1 A General Formula for Algorithms
13.2.2 Type of Optimality
13.2.3 Parameter Tuning
13.3 Framework for Self-Tuning Algorithms
13.3.1 Hyperoptimization
13.3.2 A Multi-Objective View
13.3.3 Self-Tuning Framework
13.4 Self-Tuning Firefly Algorithm
13.5 Some Remarks
References
Chapter 14 : How to Deal With Constraints
Abstract
Keywords
14.1 Introduction and Overview
14.2 Method of Lagrange Multipliers
14.3 KKT Conditions
14.4 Classic Constraint-Handling Techniques
14.4.1 Penalty Method
14.4.2 Barrier Function Method
14.4.3 Adaptive and Dynamic Penalty Method
14.4.4 Equality With Tolerance
14.5 Modern Constraint-Handling Techniques
14.5.1 Feasibility Rules
14.5.2 Stochastic Ranking
14.5.3 The ϵ -Constrained Approach
14.5.4 Multi-Objective Approach to Constraints
14.5.5 Recent Developments
14.6 An Example: Pressure Vessel Design
14.7 Concluding Remarks
References
Chapter 15 : Multi-Objective Optimization
Abstract
Keywords
15.1 Multi-Objective Optimization
15.2 Pareto Optimality
15.3 Weighted Sum Method
15.4 Utility Method
15.5 The ϵ -Constraint Method
15.6 Nature-Inspired Metaheuristics
15.6.1 Metaheuristic Approaches
15.6.2 NSGA-II
15.7 Recent Trends
References
Chapter 16 : Data Mining and Deep Learning
Abstract
Keywords
16.1 Introduction to Data Mining
16.2 Clustering
16.2.1 Clustering and Distances
16.2.2 The kNN Algorithm
16.2.3 The k -Means Algorithm
16.2.4 Nature-Inspired Algorithms for Data Analysis
16.3 Support Vector Machine
16.3.1 Linear SVM
16.3.2 Nonlinear SVM
16.3.3 Nature-Inspired Algorithms for SVM
16.4 Artificial Neural Networks
16.4.1 Machine Learning
16.4.2 Neural Models
16.4.3 Neural Networks
16.5 Optimizers for Machine Learning
16.6 Deep Learning
16.6.1 Recent Developments
16.6.2 Hyperparameter Tuning
16.6.3 Nature-Inspired Algorithms for Deep Learning
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