Nature-Inspired Computing and Optimization

Theory and Applications

  • Srikanta Patnaik
  • Xin-She Yang
  • Kazumi Nakamatsu

Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 10)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. David Green, Aldeida Aleti, Julian Garcia
    Pages 1-27
  3. S. Alwadani, F. Mendivil, R. Shonkwiler
    Pages 95-122
  4. James Alexander Hughes, Sheridan Houghten, Daniel Ashlock
    Pages 123-149
  5. Cristina Bianca Pop, Viorica Rozina Chifu, Ioan Salomie, Dalma Szonja Racz, Razvan Mircea Bonta
    Pages 151-183
  6. Gopi Ram, Durbadal Mandal, S. P. Ghoshal, Rajib Kar
    Pages 185-215
  7. Yuki Koizumi, Shin’ichi Arakawa, Masayuki Murata
    Pages 305-328
  8. Hamza Boudjefdjouf, Francesco de Paulis, Houssem Bouchekara, Antonio Orlandi, Mostafa K. Smail
    Pages 329-348
  9. Mahdi Khosravy, Neeraj Gupta, Ninoslav Marina, Ishwar K. Sethi, Mohammad Reza Asharif
    Pages 349-379
  10. Mahdi Khosravy, Neeraj Gupta, Ninoslav Marina, Ishwar K. Sethi, Mohammad Reza Asharif
    Pages 381-407
  11. Amrita Chakraborty, Arpan Kumar Kar
    Pages 475-494

About this book


The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.


Antenna Array Design Bio-Inspired Computing Ant Colony Optimisation Bat Algorithm Classifier System Cuckoo Search Economic Dispatch Flower Pollination Algorithm Genetic Algorithm Image Enhancement Swarm Intelligence Nature-Inspired Algorithm Wireless Sensor Network Multi-Agent Systems Permutation Problems Particle Swarm Optimization Resource Planning Swarm-Based Heuristics Multi-Objective Optimization

Editors and affiliations

  • Srikanta Patnaik
    • 1
  • Xin-She Yang
    • 2
  • Kazumi Nakamatsu
    • 3
  1. 1.Dept of Computer Science and EngineeringSOA University Dept of Computer Science and EngineeringBhubaneswarIndia
  2. 2.School of Science and TechnologyMiddlesex University School of Science and TechnologyLondonUnited Kingdom
  3. 3.School of H.S.E.University of Hyogo School of H.S.E.HimejiJapan

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-50919-8
  • Online ISBN 978-3-319-50920-4
  • Series Print ISSN 2196-7326
  • Series Online ISSN 2196-7334
  • Buy this book on publisher's site
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