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Introduction to nature-inspired optimization

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to nature-inspired optimization/ George Lindfield, John Penny.
Author:	Lindfield, G. R.
other author:	Penny, John.
Published:	London :Academic Press, : 2017.,
Description:	1 online resource.
Subject:	Mathematical optimization. -
Online resource:	https://www.sciencedirect.com/science/book/9780128036365
ISBN:	9780128036662 (electronic bk.)

Introduction to nature-inspired optimization
Lindfield, G. R.

Introduction to nature-inspired optimization[electronic resource] /George Lindfield, John Penny. - First edition. - London :Academic Press,2017. - 1 online resource.

Includes bibliographical references and index.

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

"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.

ISBN: 9780128036662 (electronic bk.)Subjects--Topical Terms:

527675
Mathematical optimization.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Introduction to nature-inspired optimization
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245 1 0 $a Introduction to nature-inspired optimization $h [electronic resource] / $c George Lindfield, John Penny.
250 $a First edition.
260 $a London : $b Academic Press, $c 2017.
300 $a 1 online resource.
504 $a Includes bibliographical references and index.
505 0 $a 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
505 8 $a 2.4 Alternative Methods of Selection for Mating2.5 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
505 8 $a 3.3 Developments of the PSO Algorithm3.4 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
505 8 $a 5.2 Description of the Fire y Inspired Optimization Algorithm5.3 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
505 8 $a 6.6 Some Applications of the Bacterial Foraging Optimization6.7 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
520 $a "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.
588 0 $a Online resource; title from PDF title page (EBSCO, viewed September 5, 2017).
650 0 $a Mathematical optimization. $3 527675
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Penny, John. $3 1292783
856 4 0 $u https://www.sciencedirect.com/science/book/9780128036365