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Advances in Metaheuristics Algorithms: Methods and Applications

  • Book
  • © 2018

Overview

Authors:
  1. Erik Cuevas

    1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

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  2. Daniel Zaldívar

    1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

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  3. Marco Pérez-Cisneros

    1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

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  • Discusses new alternative metaheuristic developments that have proven to be effective in their application to several complex problems
  • Helps researchers, lecturers, engineers, and practitioners solve their own optimization problems
  • Is well-structured so that each chapter can be read independently from the others

Part of the book series: Studies in Computational Intelligence (SCI, volume 775)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Introduction

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 1-8

  3. The Metaheuristic Algorithm of the Social-Spider

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 9-33

  4. Calibration of Fractional Fuzzy Controllers by Using the Social-Spider Method

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 35-55

  5. The Metaheuristic Algorithm of the Locust-Search

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 57-76

  6. Identification of Fractional Chaotic Systems by Using the Locust Search Algorithm

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 77-92

  7. The States of Matter Search (SMS)

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 93-118

  8. Multimodal States of Matter Search

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 119-165

  9. Metaheuristic Algorithms Based on Fuzzy Logic

    • Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 167-218
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Keywords

About this book

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip thoseof the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.

Authors and Affiliations

  • CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

Bibliographic Information

  • Book Title: Advances in Metaheuristics Algorithms: Methods and Applications

  • Authors: Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-89309-9

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG, part of Springer Nature 2018

  • Hardcover ISBN: 978-3-319-89308-2Published: 20 April 2018

  • Softcover ISBN: 978-3-030-07736-5Published: 14 December 2018

  • eBook ISBN: 978-3-319-89309-9Published: 10 April 2018

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIV, 218

  • Number of Illustrations: 35 b/w illustrations, 13 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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