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Full 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.
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
Index
