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Metaheuristics for Data Clustering and Image Segmentation

  • Meera Ramadas
  • Ajith Abraham

Part of the Intelligent Systems Reference Library book series (ISRL, volume 152)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Meera Ramadas, Ajith Abraham
    Pages 1-5
  3. Meera Ramadas, Ajith Abraham
    Pages 7-55
  4. Meera Ramadas, Ajith Abraham
    Pages 57-65
  5. Meera Ramadas, Ajith Abraham
    Pages 95-119
  6. Meera Ramadas, Ajith Abraham
    Pages 137-153
  7. Meera Ramadas, Ajith Abraham
    Pages 155-156
  8. Back Matter
    Pages 157-163

About this book

Introduction

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.


Keywords

Computational Intelligence Flower Pollination Algorithm Differential Evolution Data Clustering Image Segmentation Metaheuristic Variants

Authors and affiliations

  • Meera Ramadas
    • 1
  • Ajith Abraham
    • 2
  1. 1.Information TechnologyUniversity College of BahrainManamaBahrain
  2. 2.Scientific Network for Innovation and Research ExcellenceMachine Intelligence Research Labs (MIR Labs)AuburnUSA

Bibliographic information