Constraint Handling in Cohort Intelligence Algorithm
- Available for pre-order. Item will ship after December 28, 2021
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 Algorithm
Chapter 2: Literature Survey on Nature Inspired Optimization Methodologies and Constraint Handling
Chapter 3: Cohort Intelligence (CI) using Static Penalty Function (SPF) Approach
Chapter 4: Constraint Handling using Self-Adaptive Penalty Function (SAPF) Approach
Chapter 5: Hybridization of Cohort Intelligence with Colliding Bodies Optimization
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 Future Recommendations
Appendix: Problem Statements of Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems
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 Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University), India