Constraint Handling in Cohort Intelligence Algorithm  book cover
1st Edition

Constraint Handling in Cohort Intelligence Algorithm

ISBN 9781032150758
Published December 27, 2021 by CRC Press
206 Pages 75 B/W Illustrations

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Book Description

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.

Table of Contents

Chapter 1: Introduction to Metaheuristic Algorithms

Chapter 2: Literature Survey on Nature Inspired Optimisation Methodologies and Constraint Handling

Chapter 3: Cohort Intelligence (CI) Using the Static Penalty Function (SPF) Approach

Chapter 4: Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach

Chapter 5: Hybridization of Cohort Intelligence with Colliding Bodies Optimisation

Chapter 6: Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable Problems

Chapter 7: Solution to Real-World Applications

Chapter 8: Conclusions and Recommendations

Appendix: Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems


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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.