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Cohort Intelligence: A Socio-inspired Optimization Method

  • Book
  • © 2017

Overview

Authors:
  1. Anand Jayant Kulkarni

    1. Odette School of Business, University of Windsor Odette School of Business, Windsor, Canada

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  2. Ganesh Krishnasamy

    1. Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya , Kuala Lumpur, Malaysia

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  3. Ajith Abraham

    1. Scientific Network for Inn and Res Exc, Machine Intell Research Labs (MIR labs) Scientific Network for Inn and Res Exc, Auburn, WA, USA

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  • Presents the core and underlying principles and analysis of the different concepts associated with an emerging socio-inspired AI optimization tool referred to as Cohort Intelligence (CI)
  • Discusses in detail the Cohort Intelligence methodology as well as several modifications for solving a variety of problems
  • Demonstrates the ability of Cohort Intelligence methodology for solving several cases of the combinatorial problems such as Traveling Salesman Problem (TSP) and Knapsack Problem (KP)
  • Includes supplementary material: sn.pub/extras

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 114)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Introduction to Optimization

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 1-7

  3. Socio-Inspired Optimization Using Cohort Intelligence

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 9-24

  4. Cohort Intelligence for Constrained Test Problems

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 25-37

  5. Modified Cohort Intelligence for Solving Machine Learning Problems

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 39-54

  6. Solution to 0–1 Knapsack Problem Using Cohort Intelligence Algorithm

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 55-74

  7. Cohort Intelligence for Solving Travelling Salesman Problems

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 75-86

  8. Solution to a New Variant of the Assignment Problem Using Cohort Intelligence Algorithm

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 87-99

  9. Solution to Sea Cargo Mix (SCM) Problem Using Cohort Intelligence Algorithm

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 101-115

  10. Solution to the Selection of Cross-Border Shippers (SCBS) Problem

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 117-129

  11. Conclusions and Future Directions

    • Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham
    Pages 131-134
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Keywords

About this book

This Volume discusses the underlying principles and analysis of the different concepts associated with an emerging socio-inspired optimization tool referred to as Cohort Intelligence (CI). CI algorithms have been coded in Matlab and are freely available from the link provided inside the book. The book demonstrates the ability of CI methodology for solving combinatorial problems such as Traveling Salesman Problem and Knapsack Problem in addition to real world applications from the healthcare, inventory, supply chain optimization and Cross-Border transportation. The inherent ability of handling constraints based on probability distribution is also revealed and proved using these problems.

 

Authors and Affiliations

  • Odette School of Business, University of Windsor Odette School of Business, Windsor, Canada

    Anand Jayant Kulkarni

  • Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya , Kuala Lumpur, Malaysia

    Ganesh Krishnasamy

  • Scientific Network for Inn and Res Exc, Machine Intell Research Labs (MIR labs) Scientific Network for Inn and Res Exc, Auburn, WA, USA

    Ajith Abraham

Bibliographic Information

  • Book Title: Cohort Intelligence: A Socio-inspired Optimization Method

  • Authors: Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham

  • Series Title: Intelligent Systems Reference Library

  • DOI: https://doi.org/10.1007/978-3-319-44254-9

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2017

  • Hardcover ISBN: 978-3-319-44253-2Published: 03 October 2016

  • Softcover ISBN: 978-3-319-83022-3Published: 15 June 2018

  • eBook ISBN: 978-3-319-44254-9Published: 22 September 2016

  • Series ISSN: 1868-4394

  • Series E-ISSN: 1868-4408

  • Edition Number: 1

  • Number of Pages: XI, 134

  • Number of Illustrations: 29 b/w illustrations

  • Topics: Computational Intelligence, Artificial Intelligence

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