Kooperativer Bibliotheksverbund

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Format: 1 online resource (xvii, 238 pages)

Edition: First edition

ISBN: 9780128036662 , 0128036664

Content: "Introduction to Nature-Inspired Optimization brings together many of the innovative mathematical methods for non-linear optimization that have their origins in the way various species behave in order to optimize their chances of survival. The book describes each method, examines their strengths and weaknesses, and where appropriate, provides the MATLAB code to give practical insight into the detailed structure of these methods and how they work. Nature-inspired algorithms emulate processes that are found in the natural world, spurring interest for optimization. Lindfield/Penny provide concise coverage to all the major algorithms, including genetic algorithms, artificial bee colony algorithms, ant colony optimization and the cuckoo search algorithm, among others. This book provides a quick reference to practicing engineers, researchers and graduate students who work in the field of optimization. Applies concepts in nature and biology to develop new algorithms for nonlinear optimizationOffers working MATLAB programs for the major algorithms described, applying them to a range of problemsProvides useful comparative studies of the algorithms, highlighting their strengths and weaknessesDiscusses the current state-of-the-field and indicates possible areas of future development."--Publisher's description

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9780128036365

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 0128036362

Language: English

Keywords: Nichtlineare Optimierung ; MATLAB

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (258 pages) : , illustrations

ISBN: 0-12-803666-4 , 0-12-803636-2

Note: Front Cover -- Introduction to Nature-Inspired Optimization -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- Notation -- 1 An Introduction to Optimization -- 1.1 Introduction -- 1.2 Classes of Optimization Problems -- 1.3 Using Calculus to Optimize a Function -- 1.4 A Brute Force Method! -- 1.5 Gradient Methods -- 1.6 Nature Inspired Optimization Algorithms -- 1.7 Randomness in Nature Inspired Algorithms -- 1.8 Testing Nature Inspired Algorithms -- 1.9 Summary -- 1.10 Problems -- 2 Evolutionary Algorithms -- 2.1 Introduction -- 2.2 Introduction to Genetic Algorithms -- 2.3 Alternative Methods of Coding -- 2.4 Alternative Methods of Selection for Mating -- 2.5 Alternative Forms of Mating -- 2.6 Alternative Forms of Mutation -- 2.7 Theoretical Background to GAs -- 2.8 Continuous or Decimal Coding -- 2.9 Selected Numerical Studies Using the Continuous GA -- 2.10 Some Applications of the Genetic Algorithm -- 2.11 Differential Evolution -- 2.12 Other Variants of Differential Evolution -- 2.13 Numerical Studies -- 2.14 Some Applications of Differential Evolution -- 2.15 Summary -- 2.16 Problems -- 3 Particle Swarm Optimization Algorithms -- 3.1 Origins of Particle Swarm Optimization -- 3.2 The PSO Algorithm -- 3.3 Developments of the PSO Algorithm -- 3.4 Selected Numerical Studies Using PSO -- 3.5 A Review of Some Relevant Developments -- 3.6 Some Applications of Particle Swarm Optimization -- 3.7 Summary -- 3.8 Problems -- 4 The Cuckoo Search Algorithm -- 4.1 Introduction -- 4.2 Description of the Cuckoo Search Algorithm -- 4.3 Modi cations of the Cuckoo Search Algorithm -- 4.4 Numerical Studies of the Cuckoo Search Algorithm -- 4.5 Extensions and Developments of the Cuckoo Search Algorithm -- 4.6 Some Applications of the Cuckoo Search Algorithm -- 4.7 Summary -- 4.8 Problems -- 5 The Fire y Algorithm -- 5.1 Introduction. , 5.2 Description of the Fire y Inspired Optimization Algorithm -- 5.3 Modi cations to the Fire y Algorithm -- 5.4 Selected Numerical Studies of the Fire y Algorithm -- 5.5 Developments of the Fire y Algorithm -- 5.6 Some Applications of the Fire y Algorithm -- 5.7 Summary -- 5.8 Reader Exercises -- 6 Bacterial Foraging Inspired Algorithm -- 6.1 Introduction -- 6.2 Description of the Bacterial Foraging Optimization Algorithm -- 6.3 Modi cations of the BFO Search Algorithm -- 6.4 Selected Numerical Studies of the BFO Search Algorithm -- 6.5 Theoretical Developments of the BFO Algorithm -- 6.6 Some Applications of the Bacterial Foraging Optimization -- 6.7 Summary -- 6.8 Problems -- 7 Arti cial Bee and Ant Colony Optimization -- 7.1 Introduction -- 7.2 The Arti cial Bee Colony Algorithm (ABC) -- 7.3 Modi cations of the Arti cial Bee Colony (ABC) Algorithm -- 7.4 Selected Numerical Studies of the Performance of the ABC Algorithm -- 7.5 Some Applications of Arti cial Bee Colony Optimization -- 7.6 Description of the Ant Colony Optimization Algorithms (ACO) -- 7.7 Modi cations of the Ant Colony Optimization (ACO) Algorithm -- 7.8 Some Applications of Ant Colony Optimization -- 7.9 Summary -- 7.10 Problems -- 8 Physics Inspired Optimization Algorithms -- 8.1 Introduction -- 8.2 Simulated Annealing -- 8.3 Some Applications of Simulated Annealing -- 8.4 The Big Bang-Big Crunch Algorithm -- 8.5 Selected Numerical Studies Using the BB-BC Algorithm -- 8.6 Some Applications of Big Bang-Big Crunch Optimization -- 8.7 The Gravitational Search Algorithm -- 8.8 Selected Numerical Studies Using the GSA -- 8.9 Some Applications of the Gravitational Search Algorithm -- 8.10 Central Force Optimization -- 8.11 Selected Numerical Studies Using CFO -- 8.12 Some Applications of Central Force Optimization -- 8.13 Central Force Optimization Compared with GSA -- 8.14 Summary. , 8.15 Problems -- 9 Integer, Constrained and Multi-Objective Optimization -- 9.1 Integer Optimization -- 9.2 Constrained Optimization -- 9.3 Introduction to Multi-objective Optimization -- 9.4 Pareto Front -- 9.5 Methods for Solving the Multi-objective Optimization Problem -- 9.6 Summary -- 10 Recent Developments and Comparative Studies -- 10.1 Introduction -- 10.2 Other Nature Inspired Optimization Algorithms -- 10.3 Comparative Studies of Selected Methods on Speci c Test Problems -- 10.4 Suggestions for Further Studies -- 10.5 Summary -- 10.6 Problems -- A Test Functions -- A.1 Introduction -- A.2 Test Functions -- B Program Listings -- Solutions to Problems -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- References -- Index -- Back Cover.

Language: English

UID:

Format: 1 online resource (258 pages) : , illustrations

ISBN: 0-12-803666-4 , 0-12-803636-2

Note: Front Cover -- Introduction to Nature-Inspired Optimization -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- Notation -- 1 An Introduction to Optimization -- 1.1 Introduction -- 1.2 Classes of Optimization Problems -- 1.3 Using Calculus to Optimize a Function -- 1.4 A Brute Force Method! -- 1.5 Gradient Methods -- 1.6 Nature Inspired Optimization Algorithms -- 1.7 Randomness in Nature Inspired Algorithms -- 1.8 Testing Nature Inspired Algorithms -- 1.9 Summary -- 1.10 Problems -- 2 Evolutionary Algorithms -- 2.1 Introduction -- 2.2 Introduction to Genetic Algorithms -- 2.3 Alternative Methods of Coding -- 2.4 Alternative Methods of Selection for Mating -- 2.5 Alternative Forms of Mating -- 2.6 Alternative Forms of Mutation -- 2.7 Theoretical Background to GAs -- 2.8 Continuous or Decimal Coding -- 2.9 Selected Numerical Studies Using the Continuous GA -- 2.10 Some Applications of the Genetic Algorithm -- 2.11 Differential Evolution -- 2.12 Other Variants of Differential Evolution -- 2.13 Numerical Studies -- 2.14 Some Applications of Differential Evolution -- 2.15 Summary -- 2.16 Problems -- 3 Particle Swarm Optimization Algorithms -- 3.1 Origins of Particle Swarm Optimization -- 3.2 The PSO Algorithm -- 3.3 Developments of the PSO Algorithm -- 3.4 Selected Numerical Studies Using PSO -- 3.5 A Review of Some Relevant Developments -- 3.6 Some Applications of Particle Swarm Optimization -- 3.7 Summary -- 3.8 Problems -- 4 The Cuckoo Search Algorithm -- 4.1 Introduction -- 4.2 Description of the Cuckoo Search Algorithm -- 4.3 Modi cations of the Cuckoo Search Algorithm -- 4.4 Numerical Studies of the Cuckoo Search Algorithm -- 4.5 Extensions and Developments of the Cuckoo Search Algorithm -- 4.6 Some Applications of the Cuckoo Search Algorithm -- 4.7 Summary -- 4.8 Problems -- 5 The Fire y Algorithm -- 5.1 Introduction. , 5.2 Description of the Fire y Inspired Optimization Algorithm -- 5.3 Modi cations to the Fire y Algorithm -- 5.4 Selected Numerical Studies of the Fire y Algorithm -- 5.5 Developments of the Fire y Algorithm -- 5.6 Some Applications of the Fire y Algorithm -- 5.7 Summary -- 5.8 Reader Exercises -- 6 Bacterial Foraging Inspired Algorithm -- 6.1 Introduction -- 6.2 Description of the Bacterial Foraging Optimization Algorithm -- 6.3 Modi cations of the BFO Search Algorithm -- 6.4 Selected Numerical Studies of the BFO Search Algorithm -- 6.5 Theoretical Developments of the BFO Algorithm -- 6.6 Some Applications of the Bacterial Foraging Optimization -- 6.7 Summary -- 6.8 Problems -- 7 Arti cial Bee and Ant Colony Optimization -- 7.1 Introduction -- 7.2 The Arti cial Bee Colony Algorithm (ABC) -- 7.3 Modi cations of the Arti cial Bee Colony (ABC) Algorithm -- 7.4 Selected Numerical Studies of the Performance of the ABC Algorithm -- 7.5 Some Applications of Arti cial Bee Colony Optimization -- 7.6 Description of the Ant Colony Optimization Algorithms (ACO) -- 7.7 Modi cations of the Ant Colony Optimization (ACO) Algorithm -- 7.8 Some Applications of Ant Colony Optimization -- 7.9 Summary -- 7.10 Problems -- 8 Physics Inspired Optimization Algorithms -- 8.1 Introduction -- 8.2 Simulated Annealing -- 8.3 Some Applications of Simulated Annealing -- 8.4 The Big Bang-Big Crunch Algorithm -- 8.5 Selected Numerical Studies Using the BB-BC Algorithm -- 8.6 Some Applications of Big Bang-Big Crunch Optimization -- 8.7 The Gravitational Search Algorithm -- 8.8 Selected Numerical Studies Using the GSA -- 8.9 Some Applications of the Gravitational Search Algorithm -- 8.10 Central Force Optimization -- 8.11 Selected Numerical Studies Using CFO -- 8.12 Some Applications of Central Force Optimization -- 8.13 Central Force Optimization Compared with GSA -- 8.14 Summary. , 8.15 Problems -- 9 Integer, Constrained and Multi-Objective Optimization -- 9.1 Integer Optimization -- 9.2 Constrained Optimization -- 9.3 Introduction to Multi-objective Optimization -- 9.4 Pareto Front -- 9.5 Methods for Solving the Multi-objective Optimization Problem -- 9.6 Summary -- 10 Recent Developments and Comparative Studies -- 10.1 Introduction -- 10.2 Other Nature Inspired Optimization Algorithms -- 10.3 Comparative Studies of Selected Methods on Speci c Test Problems -- 10.4 Suggestions for Further Studies -- 10.5 Summary -- 10.6 Problems -- A Test Functions -- A.1 Introduction -- A.2 Test Functions -- B Program Listings -- Solutions to Problems -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- References -- Index -- Back Cover.

Language: English

UID:

Format: 1 online resource (258 pages) : , illustrations

ISBN: 0-12-803666-4 , 0-12-803636-2

Note: Front Cover -- Introduction to Nature-Inspired Optimization -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- Notation -- 1 An Introduction to Optimization -- 1.1 Introduction -- 1.2 Classes of Optimization Problems -- 1.3 Using Calculus to Optimize a Function -- 1.4 A Brute Force Method! -- 1.5 Gradient Methods -- 1.6 Nature Inspired Optimization Algorithms -- 1.7 Randomness in Nature Inspired Algorithms -- 1.8 Testing Nature Inspired Algorithms -- 1.9 Summary -- 1.10 Problems -- 2 Evolutionary Algorithms -- 2.1 Introduction -- 2.2 Introduction to Genetic Algorithms -- 2.3 Alternative Methods of Coding -- 2.4 Alternative Methods of Selection for Mating -- 2.5 Alternative Forms of Mating -- 2.6 Alternative Forms of Mutation -- 2.7 Theoretical Background to GAs -- 2.8 Continuous or Decimal Coding -- 2.9 Selected Numerical Studies Using the Continuous GA -- 2.10 Some Applications of the Genetic Algorithm -- 2.11 Differential Evolution -- 2.12 Other Variants of Differential Evolution -- 2.13 Numerical Studies -- 2.14 Some Applications of Differential Evolution -- 2.15 Summary -- 2.16 Problems -- 3 Particle Swarm Optimization Algorithms -- 3.1 Origins of Particle Swarm Optimization -- 3.2 The PSO Algorithm -- 3.3 Developments of the PSO Algorithm -- 3.4 Selected Numerical Studies Using PSO -- 3.5 A Review of Some Relevant Developments -- 3.6 Some Applications of Particle Swarm Optimization -- 3.7 Summary -- 3.8 Problems -- 4 The Cuckoo Search Algorithm -- 4.1 Introduction -- 4.2 Description of the Cuckoo Search Algorithm -- 4.3 Modi cations of the Cuckoo Search Algorithm -- 4.4 Numerical Studies of the Cuckoo Search Algorithm -- 4.5 Extensions and Developments of the Cuckoo Search Algorithm -- 4.6 Some Applications of the Cuckoo Search Algorithm -- 4.7 Summary -- 4.8 Problems -- 5 The Fire y Algorithm -- 5.1 Introduction. , 5.2 Description of the Fire y Inspired Optimization Algorithm -- 5.3 Modi cations to the Fire y Algorithm -- 5.4 Selected Numerical Studies of the Fire y Algorithm -- 5.5 Developments of the Fire y Algorithm -- 5.6 Some Applications of the Fire y Algorithm -- 5.7 Summary -- 5.8 Reader Exercises -- 6 Bacterial Foraging Inspired Algorithm -- 6.1 Introduction -- 6.2 Description of the Bacterial Foraging Optimization Algorithm -- 6.3 Modi cations of the BFO Search Algorithm -- 6.4 Selected Numerical Studies of the BFO Search Algorithm -- 6.5 Theoretical Developments of the BFO Algorithm -- 6.6 Some Applications of the Bacterial Foraging Optimization -- 6.7 Summary -- 6.8 Problems -- 7 Arti cial Bee and Ant Colony Optimization -- 7.1 Introduction -- 7.2 The Arti cial Bee Colony Algorithm (ABC) -- 7.3 Modi cations of the Arti cial Bee Colony (ABC) Algorithm -- 7.4 Selected Numerical Studies of the Performance of the ABC Algorithm -- 7.5 Some Applications of Arti cial Bee Colony Optimization -- 7.6 Description of the Ant Colony Optimization Algorithms (ACO) -- 7.7 Modi cations of the Ant Colony Optimization (ACO) Algorithm -- 7.8 Some Applications of Ant Colony Optimization -- 7.9 Summary -- 7.10 Problems -- 8 Physics Inspired Optimization Algorithms -- 8.1 Introduction -- 8.2 Simulated Annealing -- 8.3 Some Applications of Simulated Annealing -- 8.4 The Big Bang-Big Crunch Algorithm -- 8.5 Selected Numerical Studies Using the BB-BC Algorithm -- 8.6 Some Applications of Big Bang-Big Crunch Optimization -- 8.7 The Gravitational Search Algorithm -- 8.8 Selected Numerical Studies Using the GSA -- 8.9 Some Applications of the Gravitational Search Algorithm -- 8.10 Central Force Optimization -- 8.11 Selected Numerical Studies Using CFO -- 8.12 Some Applications of Central Force Optimization -- 8.13 Central Force Optimization Compared with GSA -- 8.14 Summary. , 8.15 Problems -- 9 Integer, Constrained and Multi-Objective Optimization -- 9.1 Integer Optimization -- 9.2 Constrained Optimization -- 9.3 Introduction to Multi-objective Optimization -- 9.4 Pareto Front -- 9.5 Methods for Solving the Multi-objective Optimization Problem -- 9.6 Summary -- 10 Recent Developments and Comparative Studies -- 10.1 Introduction -- 10.2 Other Nature Inspired Optimization Algorithms -- 10.3 Comparative Studies of Selected Methods on Speci c Test Problems -- 10.4 Suggestions for Further Studies -- 10.5 Summary -- 10.6 Problems -- A Test Functions -- A.1 Introduction -- A.2 Test Functions -- B Program Listings -- Solutions to Problems -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- References -- Index -- Back Cover.

Language: English