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Foraging-Inspired Optimisation Algorithms

  • Anthony Brabazon
  • Seán McGarraghy

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages I-XVIII
  2. Anthony Brabazon, Seán McGarraghy
    Pages 1-17
  3. Perspectives on Foraging

    1. Front Matter
      Pages 19-21
    2. Anthony Brabazon, Seán McGarraghy
      Pages 23-44
    3. Anthony Brabazon, Seán McGarraghy
      Pages 45-63
    4. Anthony Brabazon, Seán McGarraghy
      Pages 65-82
  4. Foraging-Inspired Algorithms for Optimisation

    1. Front Matter
      Pages 83-85
    2. Anthony Brabazon, Seán McGarraghy
      Pages 87-101
  5. Vertebrates

    1. Front Matter
      Pages 103-105
    2. Anthony Brabazon, Seán McGarraghy
      Pages 107-136
    3. Anthony Brabazon, Seán McGarraghy
      Pages 137-146
    4. Anthony Brabazon, Seán McGarraghy
      Pages 147-166
  6. Invertebrates

    1. Front Matter
      Pages 167-169
    2. Anthony Brabazon, Seán McGarraghy
      Pages 171-201
    3. Anthony Brabazon, Seán McGarraghy
      Pages 203-219
    4. Anthony Brabazon, Seán McGarraghy
      Pages 221-235
    5. Anthony Brabazon, Seán McGarraghy
      Pages 237-251
    6. Anthony Brabazon, Seán McGarraghy
      Pages 253-262
  7. Nonneuronal Organisms

    1. Front Matter
      Pages 263-266
    2. Anthony Brabazon, Seán McGarraghy
      Pages 267-295
    3. Anthony Brabazon, Seán McGarraghy
      Pages 297-330
    4. Anthony Brabazon, Seán McGarraghy
      Pages 331-380
  8. Algorithms Derived from Formal Models of Foraging

    1. Front Matter
      Pages 381-383
    2. Anthony Brabazon, Seán McGarraghy
      Pages 385-403
  9. Evolving a Foraging Strategy

    1. Front Matter
      Pages 405-407
    2. Anthony Brabazon, Seán McGarraghy
      Pages 409-419
  10. Foraging Algorithms: The Future

    1. Front Matter
      Pages 421-423
    2. Anthony Brabazon, Seán McGarraghy
      Pages 425-433
  11. Back Matter
    Pages 435-478

About this book

Introduction

This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor. The authors first offer perspectives on foraging and foraging-inspired algorithms for optimization, they then explain the techniques inspired by the behaviors of vertebrates, invertebrates, and non-neuronal organisms, and they then discuss algorithms based on formal models of foraging, how to evolve a foraging strategy, and likely future developments.

No prior knowledge of natural computing is assumed. This book will be of particular interest to graduate students, academics and practitioners in computer science, informatics, data science, management science, and other application domains.

Keywords

Foraging Social Learning Foraging Algorithms Animal Behavior Ant Foraging Algorithm Bioluminescence Algorithms Bacteria Inspired Algorithms Chemotaxis Optimization Search Learning Natural Computing Genetic Programming Evolutionary Computing Slime Mould Plant Foraging Group Search Predatory Search Heuristics Honeybees

Authors and affiliations

  • Anthony Brabazon
    • 1
  • Seán McGarraghy
    • 2
  1. 1.School of BusinessUniversity College DublinDublinIreland
  2. 2.UCD Centre for Business AnalyticsUniversity College DublinDublinIreland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-59156-8
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-59155-1
  • Online ISBN 978-3-319-59156-8
  • Series Print ISSN 1619-7127
  • Buy this book on publisher's site
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