Nature-inspired optimization algorithms /:
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-cho...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London [England] ; Waltham [Massachusetts] :
Elsevier,
2014.
|
| Ausgabe: | First edition. |
| Schriftenreihe: | Elsevier insights.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm. |
| Beschreibung: | 1 online resource (276 pages) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9780124167452 0124167454 0124167438 9780124167438 |
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| 520 | |a 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 and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
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| author | Yang, Xin-She |
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| 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. |
| ctrlnum | (OCoLC)874179091 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3 |
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| discipline | Informatik |
| edition | First edition. |
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tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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 and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">6.3 Variants6.4 Choice of Parameters; 6.5 Convergence Analysis; 6.6 Implementation; References; 7 Particle Swarm Optimization; 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| id | ZDB-4-EBA-ocn874179091 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:25:52Z |
| institution | BVB |
| isbn | 9780124167452 0124167454 0124167438 9780124167438 |
| language | English |
| oclc_num | 874179091 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (276 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Elsevier, |
| record_format | marc |
| series | Elsevier insights. |
| series2 | Elsevier insights |
| spelling | Yang, Xin-She. http://id.loc.gov/authorities/names/nb2006017401 Nature-inspired optimization algorithms / Xin-She Yang. First edition. London [England] ; Waltham [Massachusetts] : Elsevier, 2014. ©2014 1 online resource (276 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Elsevier insights Includes bibliographical references. Print version record. 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 and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm. 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. English. Computer algorithms. http://id.loc.gov/authorities/subjects/sh91000149 Parallel processing (Electronic computers) http://id.loc.gov/authorities/subjects/sh85097826 Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Algorithms https://id.nlm.nih.gov/mesh/D000465 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Algorithmes. Parallélisme (Informatique) Traitement réparti. Intelligence artificielle. algorithms. aat artificial intelligence. aat COMPUTERS General. bisacsh Artificial intelligence fast Computer algorithms fast Electronic data processing Distributed processing fast Parallel processing (Electronic computers) fast has work: Nature-inspired optimization algorithms (Text) https://id.oclc.org/worldcat/entity/E39PCG6TpR44r78QXc4qr9WMT3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Yang, Xin-She. Nature-inspired optimization algorithms. First edition. London, England ; Waltham, Massachusetts : Elsevier, ©2014 xii, 263 pages 9780124167438 Elsevier insights. http://id.loc.gov/authorities/names/no2010053011 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=574809 Volltext |
| spellingShingle | Yang, Xin-She Nature-inspired optimization algorithms / Elsevier insights. 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. Computer algorithms. http://id.loc.gov/authorities/subjects/sh91000149 Parallel processing (Electronic computers) http://id.loc.gov/authorities/subjects/sh85097826 Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Algorithms https://id.nlm.nih.gov/mesh/D000465 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Algorithmes. Parallélisme (Informatique) Traitement réparti. Intelligence artificielle. algorithms. aat artificial intelligence. aat COMPUTERS General. bisacsh Artificial intelligence fast Computer algorithms fast Electronic data processing Distributed processing fast Parallel processing (Electronic computers) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh91000149 http://id.loc.gov/authorities/subjects/sh85097826 http://id.loc.gov/authorities/subjects/sh85042293 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D000465 https://id.nlm.nih.gov/mesh/D001185 |
| title | Nature-inspired optimization algorithms / |
| title_auth | Nature-inspired optimization algorithms / |
| title_exact_search | Nature-inspired optimization algorithms / |
| title_full | Nature-inspired optimization algorithms / Xin-She Yang. |
| title_fullStr | Nature-inspired optimization algorithms / Xin-She Yang. |
| title_full_unstemmed | Nature-inspired optimization algorithms / Xin-She Yang. |
| title_short | Nature-inspired optimization algorithms / |
| title_sort | nature inspired optimization algorithms |
| topic | Computer algorithms. http://id.loc.gov/authorities/subjects/sh91000149 Parallel processing (Electronic computers) http://id.loc.gov/authorities/subjects/sh85097826 Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Algorithms https://id.nlm.nih.gov/mesh/D000465 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Algorithmes. Parallélisme (Informatique) Traitement réparti. Intelligence artificielle. algorithms. aat artificial intelligence. aat COMPUTERS General. bisacsh Artificial intelligence fast Computer algorithms fast Electronic data processing Distributed processing fast Parallel processing (Electronic computers) fast |
| topic_facet | Computer algorithms. Parallel processing (Electronic computers) Electronic data processing Distributed processing. Artificial intelligence. Algorithms Artificial Intelligence Algorithmes. Parallélisme (Informatique) Traitement réparti. Intelligence artificielle. algorithms. artificial intelligence. COMPUTERS General. Artificial intelligence Computer algorithms Electronic data processing Distributed processing |
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| work_keys_str_mv | AT yangxinshe natureinspiredoptimizationalgorithms |