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Self-Organizing Migrating Algorithm

Methodology and Implementation

  • Book
  • © 2016

Overview

Editors:
  1. Donald Davendra

    1. Faculty of Ele. Eng. & Computer Sci, Dep. of Comp. Sci. Central Washington Un, Ellensburg, Czech Republic

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  2. Ivan Zelinka

    1. Faculty of Ele.Eng. & Comp. Sci., VŠB-Technical University of Ostrav, Ostrava-Poruba, Czech Republic

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  • 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)

  1. Front Matter

    Pages i-xviii
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  2. Methodology

    1. Front Matter

      Pages 1-1
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    2. SOMA—Self-organizing Migrating Algorithm

      • Ivan Zelinka
      Pages 3-49

    3. DSOMA—Discrete Self Organising Migrating Algorithm

      • Donald Davendra, Ivan Zelinka, Michal Pluhacek, Roman Senkerik
      Pages 51-63

  3. Implementation

    1. Front Matter

      Pages 65-65
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    2. SOMA and Strange Dynamics

      • Ivan Zelinka
      Pages 67-82

    3. Multi-objective Self-organizing Migrating Algorithm

      • Petr Kadlec, Zbyněk Raida
      Pages 83-103

    4. Multi-objective Design of EM Components

      • Petr Kadlec, Zbyněk Raida
      Pages 105-119

    5. Utilization of Parallel Computing for Discrete Self-organizing Migration Algorithm

      • Marek Běhálek, Petr Gajdoš, Donald Davendra
      Pages 121-145

    6. C-SOMAQI: Self Organizing Migrating Algorithm with Quadratic Interpolation Crossover Operator for Constrained Global Optimization

      • Dipti Singh, Seema Agrawal, Kusum Deep
      Pages 147-165

    7. Optimization of Directional Overcurrent Relay Times Using C-SOMGA

      • Kusum Deep, Dipti Singh
      Pages 167-186

    8. SOMGA for Large Scale Function Optimization and Its Application

      • Dipti Singh, Kusum Deep
      Pages 187-205

    9. Solving the Routing Problems with Time Windows

      • Zuzana Čičková, Ivan Brezina, Juraj Pekár
      Pages 207-236

    10. SOMA in Financial Modeling

      • Juraj Pekár, Zuzana Čičková, Ivan Brezina
      Pages 237-253

    11. Setting of Control Parameters of SOMA on the Base of Statistics

      • Zuzana Čičková, Martin Lukáčik
      Pages 255-275

    12. Inspired in SOMA: Perturbation Vector Embedded into the Chaotic PSO Algorithm Driven by Lozi Chaotic Map

      • Michal Pluhacek, Ivan Zelinka, Roman Senkerik, Donald Davendra
      Pages 277-289
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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

  • Faculty of Ele. Eng. & Computer Sci, Dep. of Comp. Sci. Central Washington Un, Ellensburg, Czech Republic

    Donald Davendra

  • Faculty of Ele.Eng. & Comp. Sci., VŠB-Technical University of Ostrav, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

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

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