Self-Organizing Migrating Algorithm [electronic resource] : Methodology and Implementation / edited by Donald Davendra, Ivan Zelinka.
Contributor(s): Davendra, Donald [editor.]
| Zelinka, Ivan [editor.] | SpringerLink (Online service).
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BookSeries: Studies in Computational Intelligence: 626Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: XVIII, 289 p. 128 illus., 87 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319281612.Subject(s): Computational intelligence | Artificial intelligence | Mathematical optimization | Computational Intelligence | Artificial Intelligence | OptimizationAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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Springer Nature eBookSummary: 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. .
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. .
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