Nature-inspired optimizers : theories, literature reviews and applications

Responsibility: Seyedali Mirjalili, Jin Song Dong, Andrew Lewis, editors.
Digital: text file; PDF
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

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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

Subjects: Evolutionary computation.; Mathematical optimization.

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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