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Multi-Objective Optimization

Evolutionary to Hybrid Framework

  • Jyotsna K. Mandal
  • Somnath Mukhopadhyay
  • Paramartha Dutta

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Saibal Majumder, Samarjit Kar, Tandra Pal
    Pages 25-54
  3. R. Chulaka Gunasekara, Chilukuri K. Mohan, Kishan Mehrotra
    Pages 115-139
  4. Saurav Mallik, Tapas Bhadra, Soumita Seth, Sanghamitra Bandyopadhyay, Jianjiao Chen
    Pages 159-180
  5. Niladri Sekhar Datta, Himadri Sekhar Dutta, Koushik Majumder, Sumana Chatterjee, Najir Abdul Wasim
    Pages 269-278

About this book

Introduction

This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms.

The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems.

Keywords

Optimization Multi-objective Science and Engineering Applications Computational Intelligence Mathematics

Editors and affiliations

  • Jyotsna K. Mandal
    • 1
  • Somnath Mukhopadhyay
    • 2
  • Paramartha Dutta
    • 3
  1. 1.University of KalyaniKalyaniIndia
  2. 2.Assam UniversitySilcharIndia
  3. 3.Visva Bharati UniversityBolpur, SantiniketanIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-1471-1
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-13-1470-4
  • Online ISBN 978-981-13-1471-1
  • Buy this book on publisher's site