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Advances in Metaheuristics Algorithms: Methods and Applications

  • Erik Cuevas
  • Daniel Zaldívar
  • Marco Pérez-Cisneros

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

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 1-8
  3. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 9-33
  4. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 35-55
  5. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 57-76
  6. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 77-92
  7. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 93-118
  8. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 119-165
  9. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros
    Pages 167-218

About this book

Introduction

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 those of 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.

Keywords

Evolutionary Computation Swarm Algorithms Metaheuristics Metaheuristic Methods Social Spider Optimization

Authors and affiliations

  • Erik Cuevas
    • 1
  • Daniel Zaldívar
    • 2
  • Marco Pérez-Cisneros
    • 3
  1. 1.CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  2. 2.CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  3. 3.CUCEIUniversidad de GuadalajaraGuadalajaraMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-89309-9
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-89308-2
  • Online ISBN 978-3-319-89309-9
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site