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Constraint Handling in Cohort Intelligence Algorithm

Constraint Handling in Cohort Intelligence Algorithm

Constraint Handling in Cohort Intelligence Algorithm Paperback - 2024

by Ishaan R. Kale

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Paperback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; This is a valuable reference to practitioners, students and researchers in the area of optimization methods. CI is investigated by solving discrete variable truss structural problems, mixed variable design engineering problems, lin
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Details

  • Title Constraint Handling in Cohort Intelligence Algorithm
  • Author Ishaan R. Kale
  • Binding Paperback
  • Condition New
  • Pages 200
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2024-10-07
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # ria9781032156576_inp
  • ISBN 9781032156576 / 1032156570
  • Weight 0.66 lbs (0.30 kg)
  • Dimensions 9.21 x 6.14 x 0.44 in (23.39 x 15.60 x 1.12 cm)
  • Category Business / Economics / Finance
  • Library of Congress subjects Artificial intelligence, Algorithms
  • Library of Congress Catalogue Number 2021036526
  • Dewey Decimal Code 006.3
  • Quantity available 623

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From the publisher

Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms.

Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined.

Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.

About the author

Ishaan R. Kale is a researcher for the Optimization and Agent Technology Research (OAT Research) Lab.

Anand J. Kulkarni is an Associate Professor at the Institute of Artificial Intelligence, MIT World Peace University, India.

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