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Fireworks Algorithm

A Novel Swarm Intelligence Optimization Method

  • Book
  • © 2015

Overview

Authors:
  1. Ying Tan

    1. Peking University, Beijing, China

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  • Reviews the state-of-the-art of firework algorithms (FWA) as a novel explosive search way for optimization

  • Offers the key operators and characteristics as well as theoretical analyses of convergence and time-complexity of FWA through stochastic Markov process

  • Presents exhaustively the key recent research into varieties of improving versions of FWA so far

  • Enriches understanding of FWA by incorporating FWA with GPU, MOO, and combinatorial optimization

  • Covers many different applications including NMF, document clustering, pattern recognition, inversion problem, and swarm robotics

  • Includes supplementary material: sn.pub/extras

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xxxix
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  2. Fundamentals and Basic Theory

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Ying Tan
      Pages 3-16

    3. Fireworks Algorithm (FWA)

      • Ying Tan
      Pages 17-35

    4. Modeling and Theoretical Analysis of FWA

      • Ying Tan
      Pages 37-58

  3. FWA Variants

    1. Front Matter

      Pages 59-59
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    2. FWA Based on Function Approximation Approaches

      • Ying Tan
      Pages 61-73

    3. FWA with Controlling Exploration and Exploitation

      • Ying Tan
      Pages 75-85

    4. Enhanced Fireworks Algorithm

      • Ying Tan
      Pages 87-102

    5. Fireworks Algorithm with Dynamic Search

      • Ying Tan
      Pages 103-117

    6. Adaptive Fireworks Algorithm

      • Ying Tan
      Pages 119-131

    7. Cooperative Fireworks Algorithm

      • Ying Tan
      Pages 133-149

    8. Hybrid Fireworks Algorithms

      • Ying Tan
      Pages 151-161

  5. Applications

    1. Front Matter

      Pages 245-245
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    2. FWA Application on Non-negative Matrix Factorization

      • Ying Tan
      Pages 247-262
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Keywords

About this book

This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modeling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metahuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.

Authors and Affiliations

  • Peking University, Beijing, China

    Ying Tan

Bibliographic Information

  • Book Title: Fireworks Algorithm

  • Book Subtitle: A Novel Swarm Intelligence Optimization Method

  • Authors: Ying Tan

  • DOI: https://doi.org/10.1007/978-3-662-46353-6

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2015

  • Hardcover ISBN: 978-3-662-46352-9Published: 20 October 2015

  • Softcover ISBN: 978-3-662-51618-8Published: 23 August 2016

  • eBook ISBN: 978-3-662-46353-6Published: 11 October 2015

  • Edition Number: 1

  • Number of Pages: XXXIX, 323

  • Topics: Artificial Intelligence, Computational Intelligence, Numeric Computing, Robotics and Automation

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