Overview
- The originators and pioneers of the Self-Organizing Migrating Algorithm SOMA present the theory and application domain of the algorithm in a concise and detailed format
- Presents the SOMA methodology and framework in an integrated way to make it accessible to a number of audiences
- Divided into a theory and applications part
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 626)
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Table of contents (13 chapters)
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Methodology
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Implementation
Keywords
About this book
This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners.
As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.
Editors and Affiliations
Bibliographic Information
Book Title: Self-Organizing Migrating Algorithm
Book Subtitle: Methodology and Implementation
Editors: Donald Davendra, Ivan Zelinka
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-28161-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-28159-9Published: 12 February 2016
Softcover ISBN: 978-3-319-80286-2Published: 30 March 2018
eBook ISBN: 978-3-319-28161-2Published: 04 February 2016
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XVIII, 289
Number of Illustrations: 41 b/w illustrations, 87 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Optimization