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Nature-Inspired Optimization Algorithms.

By: Yang, Xin-She.
Material type: TextTextSeries: eBooks on Demand.Publisher: Burlington : Elsevier Science, 2014Description: 1 online resource (277 p.).ISBN: 9780124167452.Subject(s): Artificial intelligence | Computer algorithms | Electronic data processing -- Distributed processing | Parallel processing (Electronic computers)Genre/Form: Electronic books.Additional physical formats: Print version:: Nature-Inspired Optimization AlgorithmsDDC classification: 006 Online resources: Click here to view this ebook.
Contents:
Half Title; Title Page; Copyright; Contents; Preface; 1 Introduction to Algorithms; 1.1 What is an Algorithm?; 1.2 Newton's Method; 1.3 Optimization; 1.3.1 Gradient-Based Algorithms; 1.3.2 Hill Climbing with Random Restart; 1.4 Search for Optimality; 1.5 No-Free-Lunch Theorems; 1.5.1 NFL Theorems; 1.5.2 Choice of Algorithms; 1.6 Nature-Inspired Metaheuristics; 1.7 A Brief History of Metaheuristics; References; 2 Analysis of Algorithms; 2.1 Introduction; 2.2 Analysis of Optimization Algorithms; 2.2.1 Algorithm as an Iterative Process; 2.2.2 An Ideal Algorithm?; 2.2.3 A Self-Organization System
2.2.4 Exploration and Exploitation2.2.5 Evolutionary Operators; 2.3 Nature-Inspired Algorithms; 2.3.1 Simulated Annealing; 2.3.2 Genetic Algorithms; 2.3.3 Differential Evolution; 2.3.4 Ant and Bee Algorithms; 2.3.5 Particle Swarm Optimization; 2.3.6 The Firefly Algorithm; 2.3.7 Cuckoo Search; 2.3.8 The Bat Algorithm; 2.3.9 Harmony Search; 2.3.10 The Flower Algorithm; 2.3.11 Other Algorithms; 2.4 Parameter Tuning and Parameter Control; 2.4.1 Parameter Tuning; 2.4.2 Hyperoptimization; 2.4.3 Multiobjective View; 2.4.4 Parameter Control; 2.5 Discussions; 2.6 Summary; References
3 Random Walks and Optimization3.1 Random Variables; 3.2 Isotropic Random Walks; 3.3 Lévy Distribution and Lévy Flights; 3.4 Optimization as Markov Chains; 3.4.1 Markov Chain; 3.4.2 Optimization as a Markov Chain; 3.5 Step Sizes and Search Efficiency; 3.5.1 Step Sizes, Stopping Criteria, and Efficiency; 3.5.2 Why Lévy Flights are More Efficient; 3.6 Modality and Intermittent Search Strategy; 3.7 Importance of Randomization; 3.7.1 Ways to Carry Out Random Walks; 3.7.2 Importance of Initialization; 3.7.3 Importance Sampling; 3.7.4 Low-Discrepancy Sequences; 3.8 Eagle Strategy
3.8.1 Basic Ideas of Eagle Strategy3.8.2 Why Eagle Strategy is So Efficient; References; 4 Simulated Annealing; 4.1 Annealing and Boltzmann Distribution; 4.2 Parameters; 4.3 SA Algorithm; 4.4 Unconstrained Optimization; 4.5 Basic Convergence Properties; 4.6 SA Behavior in Practice; 4.7 Stochastic Tunneling; References; 5 Genetic Algorithms; 5.1 Introduction; 5.2 Genetic Algorithms; 5.3 Role of Genetic Operators; 5.4 Choice of Parameters; 5.5 GA Variants; 5.6 Schema Theorem; 5.7 Convergence Analysis; References; 6 Differential Evolution; 6.1 Introduction; 6.2 Differential Evolution
6.3 Variants6.4 Choice of Parameters; 6.5 Convergence Analysis; 6.6 Implementation; References; 7 Particle Swarm Optimization; 7.1 Swarm Intelligence; 7.2 PSO Algorithm; 7.3 Accelerated PSO; 7.4 Implementation; 7.5 Convergence Analysis; 7.5.1 Dynamical System; 7.5.2 Markov Chain Approach; 7.6 Binary PSO; References; 8 Firefly Algorithms; 8.1 The Firefly Algorithm; 8.1.1 Firefly Behavior; 8.1.2 Standard Firefly Algorithm; 8.1.3 Variations of Light Intensity and Attractiveness; 8.1.4 Controlling Randomization; 8.2 Algorithm Analysis; 8.2.1 Scalings and Limiting Cases
8.2.2 Attraction and Diffusion
Summary: Nature-Inspired Optimization Algorithms provides a systematic 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 well-chosen 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
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Electronic Book UT Tyler Online
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Q335 | QA76.58 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1637335 Available EBL1637335

Half Title; Title Page; Copyright; Contents; Preface; 1 Introduction to Algorithms; 1.1 What is an Algorithm?; 1.2 Newton's Method; 1.3 Optimization; 1.3.1 Gradient-Based Algorithms; 1.3.2 Hill Climbing with Random Restart; 1.4 Search for Optimality; 1.5 No-Free-Lunch Theorems; 1.5.1 NFL Theorems; 1.5.2 Choice of Algorithms; 1.6 Nature-Inspired Metaheuristics; 1.7 A Brief History of Metaheuristics; References; 2 Analysis of Algorithms; 2.1 Introduction; 2.2 Analysis of Optimization Algorithms; 2.2.1 Algorithm as an Iterative Process; 2.2.2 An Ideal Algorithm?; 2.2.3 A Self-Organization System

2.2.4 Exploration and Exploitation2.2.5 Evolutionary Operators; 2.3 Nature-Inspired Algorithms; 2.3.1 Simulated Annealing; 2.3.2 Genetic Algorithms; 2.3.3 Differential Evolution; 2.3.4 Ant and Bee Algorithms; 2.3.5 Particle Swarm Optimization; 2.3.6 The Firefly Algorithm; 2.3.7 Cuckoo Search; 2.3.8 The Bat Algorithm; 2.3.9 Harmony Search; 2.3.10 The Flower Algorithm; 2.3.11 Other Algorithms; 2.4 Parameter Tuning and Parameter Control; 2.4.1 Parameter Tuning; 2.4.2 Hyperoptimization; 2.4.3 Multiobjective View; 2.4.4 Parameter Control; 2.5 Discussions; 2.6 Summary; References

3 Random Walks and Optimization3.1 Random Variables; 3.2 Isotropic Random Walks; 3.3 Lévy Distribution and Lévy Flights; 3.4 Optimization as Markov Chains; 3.4.1 Markov Chain; 3.4.2 Optimization as a Markov Chain; 3.5 Step Sizes and Search Efficiency; 3.5.1 Step Sizes, Stopping Criteria, and Efficiency; 3.5.2 Why Lévy Flights are More Efficient; 3.6 Modality and Intermittent Search Strategy; 3.7 Importance of Randomization; 3.7.1 Ways to Carry Out Random Walks; 3.7.2 Importance of Initialization; 3.7.3 Importance Sampling; 3.7.4 Low-Discrepancy Sequences; 3.8 Eagle Strategy

3.8.1 Basic Ideas of Eagle Strategy3.8.2 Why Eagle Strategy is So Efficient; References; 4 Simulated Annealing; 4.1 Annealing and Boltzmann Distribution; 4.2 Parameters; 4.3 SA Algorithm; 4.4 Unconstrained Optimization; 4.5 Basic Convergence Properties; 4.6 SA Behavior in Practice; 4.7 Stochastic Tunneling; References; 5 Genetic Algorithms; 5.1 Introduction; 5.2 Genetic Algorithms; 5.3 Role of Genetic Operators; 5.4 Choice of Parameters; 5.5 GA Variants; 5.6 Schema Theorem; 5.7 Convergence Analysis; References; 6 Differential Evolution; 6.1 Introduction; 6.2 Differential Evolution

6.3 Variants6.4 Choice of Parameters; 6.5 Convergence Analysis; 6.6 Implementation; References; 7 Particle Swarm Optimization; 7.1 Swarm Intelligence; 7.2 PSO Algorithm; 7.3 Accelerated PSO; 7.4 Implementation; 7.5 Convergence Analysis; 7.5.1 Dynamical System; 7.5.2 Markov Chain Approach; 7.6 Binary PSO; References; 8 Firefly Algorithms; 8.1 The Firefly Algorithm; 8.1.1 Firefly Behavior; 8.1.2 Standard Firefly Algorithm; 8.1.3 Variations of Light Intensity and Attractiveness; 8.1.4 Controlling Randomization; 8.2 Algorithm Analysis; 8.2.1 Scalings and Limiting Cases

8.2.2 Attraction and Diffusion

Nature-Inspired Optimization Algorithms provides a systematic 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 well-chosen 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

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