Nature-inspired optimizers : theories, literature reviews and applications
- Responsibility
- Seyedali Mirjalili, Jin Song Dong, Andrew Lewis, editors.
- Digital
- text file
- Publication
- Cham, Switzerland : Springer, 2020.
- Physical description
- 1 online resource (xvi, 238 pages) : illustrations (some color)
- Series
- Studies in computational intelligence ; v. 811. 1860-949X
Online
Available online
More options
Description
Creators/Contributors
- Contributor
- Mirjalili, Seyedali, editor.
- Dong, Jin Song, 1967- editor.
- Lewis, Andrew, editor.
Contents/Summary
- Bibliography
- Includes bibliographical references.
- Contents
-
- Preface
- Chapter 1. Introduction to Nature-inspired Algorithms
- Chapter 2. Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning.-Chapter 3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Network
- Chapter 4. Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection
- Chapter 5. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction etc.
- Summary
- This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.
Subjects
Bibliographic information
- Publication date
- 2020
- Series
- Studies in computational intelligence, 1860-949X ; volume 811
- ISBN
- 9783030121273 (electronic bk.)
- 3030121275 (electronic bk.)
- 9783030121280 (print)
- 3030121283
- 9783030121297 (print)
- 3030121291
- 9783030121266 (print)
- 3030121267 (print)
- DOI
- 10.1007/978-3-030-12127-3
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