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Advances in Swarm Intelligence

Variations and Adaptations for Optimization Problems

  • Book
  • © 2023

Overview

Editors:
  1. Anupam Biswas

    1. Department of CS and Engineering, National Institute Of Technology Silchar, Cachar, India

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  2. Can B. Kalayci

    1. Department of Industrial Engineering, Pamukkale Üniversitesi Hastaneleri, Pamukkale, Turkey

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    You can also search for this editor in PubMed   Google Scholar

  3. Seyedali Mirjalili

    1. Centre for AI Research and Optimisation, Torrens University Australia, Brisbane, Australia

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  • Focuses on the studies of existing Swarm Intelligence techniques, their variants and applications
  • Presents studies covering basic introduction and analysis of key components of the algorithms
  • Presents applications incorporating the ways of designing objective functions and solution representation

Part of the book series: Studies in Computational Intelligence (SCI, volume 1054)

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Table of contents (22 chapters)

  1. Front Matter

    Pages i-ix
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  2. State-of-the-Art

    1. Front Matter

      Pages 1-1
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    2. A Brief Tutorial on Optimization Problems, Optimization Algorithms, Meta-Heuristics, and Swarm Intelligence

      • Seyedali Mirjalili, Can B. Kalayci, Anupam Biswas
      Pages 3-14

    3. Introductory Review of Swarm Intelligence Techniques

      • Thounaojam Chinglemba, Soujanyo Biswas, Debashish Malakar, Vivek Meena, Debojyoti Sarkar, Anupam Biswas
      Pages 15-35

    4. Swarm Intelligence for Deep Learning: Concepts, Challenges and Recent Trends

      • Vandana Bharti, Bhaskar Biswas, Kaushal Kumar Shukla
      Pages 37-57

    5. Advances on Particle Swarm Optimization in Solving Discrete Optimization Problems

      • M. A. H. Akhand, Md. Masudur Rahman, Nazmul Siddique
      Pages 59-88

    6. Performance Analysis of Hybrid Memory Based Dragonfly Algorithm in Engineering Problems

      • Sanjoy Debnath, Ravi Singh Kurmvanshi, Wasim Arif
      Pages 89-106

  3. Engineering Problems

    1. Front Matter

      Pages 107-107
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    2. Optimum Design and Tuning Applications in Structural Engineering via Swarm Intelligence

      • Gebrail Bekdaş, Sinan Melih Nigdeli, Aylin Ece Kayabekir
      Pages 109-134

    3. Bee Colony Optimization with Applications in Transportation Engineering

      • Dušan Teodorović, Miloš Nikolić, Milica Šelmić, Ivana Jovanović
      Pages 135-152

    4. Application of Swarm Based Approaches for Elastic Modulus Prediction of Recycled Aggregate Concrete

      • Harish Narayana, Prashanth Janardhan
      Pages 153-167

    5. Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization for Optimizing Photonics Crystals

      • Seyed Mohammad Mirjalili, Seyedeh Zahra Mirjalili, Nima Khodadadi, Vaclav Snasel, Seyedali Mirjalili
      Pages 169-179

    6. Intelligent and Reliable Cognitive 5G Networks Using Whale Optimization Techniques

      • Jasiya Bashir, Javaid Ahmad Sheikh, Zahid A. Bhat
      Pages 181-196

  4. Machine Learning

    1. Front Matter

      Pages 197-197
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    2. Automatic Data Clustering Using Farmland Fertility Metaheuristic Algorithm

      • Farhad Soleimanian Gharehchopogh, Human Shayanfar
      Pages 199-215

    3. A Comprehensive Review of the Firefly Algorithms for Data Clustering

      • MKA Ariyaratne, TGI Fernando
      Pages 217-239

    4. A Hybrid African Vulture Optimization Algorithm and Harmony Search: Algorithm and Application in Clustering

      • Farhad Soleimanian Gharehchopogh, Benyamin Abdollahzadeh, Nima Khodadadi, Seyedali Mirjalili
      Pages 241-254

    5. Estimation Models for Optimum Design of Structural Engineering Problems via Swarm-Intelligence Based Algorithms and Artificial Neural Networks

      • Melda Yücel, Sinan Melih Nigdeli, Gebrail Bekdaş
      Pages 255-267

    6. A Novel Codebook Generation by Lévy Flight Based Firefly Algorithm

      • Ilker Kilic
      Pages 269-284

    7. Novel Chaotic Best Firefly Algorithm: COVID-19 Fake News Detection Application

      • Miodrag Zivkovic, Aleksandar Petrovic, K. Venkatachalam, Ivana Strumberger, Hothefa Shaker Jassim, Nebojsa Bacanin
      Pages 285-305
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Keywords

About this book

Swarm Intelligence (SI) has grown significantly, both from the perspective of algorithmic development and applications covering almost all disciplines science and technology. This book emphasizes the studies of existing SI techniques, their variants and applications. The book also contains reviews of new developments in SI techniques and hybridizations. Algorithm specific studies covering basic introduction and analysis of key components of these algorithms, such as convergence, balance of solution accuracy, computational costs, tuning and control of parameters. Application specific studies incorporating the ways of designing objective functions, solution representation and constraint handling. The book also includes studies on application domain specific adaptations in the SI techniques. The book will be beneficial for academicians and researchers from various disciplines of engineering and science working in applications of SI and other optimization problems. 

Editors and Affiliations

  • Department of CS and Engineering, National Institute Of Technology Silchar, Cachar, India

    Anupam Biswas

  • Department of Industrial Engineering, Pamukkale Üniversitesi Hastaneleri, Pamukkale, Turkey

    Can B. Kalayci

  • Centre for AI Research and Optimisation, Torrens University Australia, Brisbane, Australia

    Seyedali Mirjalili

Bibliographic Information

  • Book Title: Advances in Swarm Intelligence

  • Book Subtitle: Variations and Adaptations for Optimization Problems

  • Editors: Anupam Biswas, Can B. Kalayci, Seyedali Mirjalili

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-031-09835-2

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Hardcover ISBN: 978-3-031-09834-5Published: 02 October 2022

  • Softcover ISBN: 978-3-031-09837-6Published: 03 October 2023

  • eBook ISBN: 978-3-031-09835-2Published: 01 October 2022

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: IX, 419

  • Number of Illustrations: 43 b/w illustrations, 72 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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