Constraint Handling in Cohort Intelligence Algorithm (Advances in Metaheuristics)

✍ Scribed by Ishaan R. Kale, Anand J. Kulkarni

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 207
• Edition: 1
• Category: Library

✦ Synopsis

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

Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Chapter 1 Introduction to Metaheuristic Algorithms
1.1 What Is a Metaheuristic Algorithm?
1.2 Design Variables
1.3 Constraint Handling
1.4 Overview of Cohort Intelligence (CI) Algorithm
1.5 Organisation of the Book
References
Chapter 2 Literature Survey On Nature Inspired Optimisation Methodologies and Constraint Handling
2.1 Classification of Nature Inspired Optimisation Techniques
2.2 Background of the Cohort Intelligence Algorithm
2.3 Literature Review On Constraint Handling Techniques
2.4 Conclusion
References
Chapter 3 Cohort Intelligence (CI) Using the Static Penalty Function (SPF) Approach
3.1 CI-SPF
3.1.1 Framework of CI
3.1.2 Static Penalty Function (SPF) Approach
3.1.3 A Round Off Integer Sampling Approach
3.2.1 Generalised Mathematical Formulation of Truss Structure Problems
3.2.2 6-Bar Truss Structure Problem
3.2.3 Pressure Vessel Design Engineering Problems
3.2.4 Linear and Nonlinear Test Problems
3.2 Test Examples
3.3 Analysis of CI Varying Number of Candidates (C) and Sampling Space Reduction Factor ()
3.4 Conclusion
References
Chapter 4 Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach
4.1 Introduction
4.2 Self-Adaptive Penalty Function (SAPF)
4.3 CI-SAPF
4.4 Problems Solved
4.4.1 6-Bar Truss Structure Problem
4.4.2 Pressure Vessel Design Engineering Problem
4.4.3 Linear and Nonlinear Test Problems
4.5 Conclusion
References
Chapter 5 Hybridization of Cohort Intelligence With Colliding Bodies Optimisation
5.1 Characteristics of CI
5.2 Colliding Bodies Optimization (CBO)
5.3 Framework of CI-SAPF-CBO
5.4 Problem Solved
5.4.1 6-Bar Truss Structure Problem
5.4.2 Pressure Vessel Design Engineering Problem
5.4.3 Linear and Nonlinear Test Problems
5.5 Conclusion
References
Chapter 6 Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable Problems
6.1 Truss Structure Problems
6.1.1 10-Bar Truss Structure
6.1.2 Spatial 25-Bar Truss Structure
6.1.3 Planer 38-Bar Truss Structure
6.1.4 Planer 45-Bar Truss Structure
6.1.5 Spatial 52-Bar Truss Structure
6.1.6 Spatial 72-Bar Truss Structure
6.2 Design Engineering Problems
6.2.1 Stepped Cantilever Beam Design Problem
6.2.2 Speed Reducer Design Problem
6.2.3 Reinforced Concrete Beam Design
6.2.4 Welded Beam Design Case 1 and Case 2
6.2.5 Multiple Disc Clutch Brake
6.2.6 Helical Compression Spring Design
6.2.7 I-Section Beam Design Problem for Minimisation of Vertical Deflection
6.2.8 Cantilever Beam
6.2.9 Compound Gear Train
6.3 Linear and Nonlinear Benchmark Test Problems
6.4 Results, Analysis and Discussion
References
Chapter 7 Solution to Real-World Applications
7.1 Multi-Pass Turning Process Problem
7.2 Multi-Pass Milling Process Problem
7.3 Conclusion
References
Chapter 8 Conclusions and Recommendations
8.1 Conclusions
8.2 Recommendations
References
Appendix Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems
A.1 Truss Structure Problems
A.1.1 Problem 1: 6-Bar Truss Structure
A.1.2 10-Bar Truss Structure
A.1.3 Spatial 25-Bar Truss Structure (Transmission Tower)
A.1.4 Planer 38-Bar Truss Structure
A.1.5 Planer 45-Bar Truss Structure
A.1.6 52-Bar Truss Structure
A.1.7 Spatial 72-Bar Truss Structure
A.2 Design Engineering Problems
A.2.1 Stepped Cantilever Beam Design Problem
A.2.2 Pressure Vessel Design Problems
A.2.3 Speed Reducer Design Problem
A.2.4 Reinforced Concrete Beam Design
A.2.5 Welded Beam Design Case 1
A.2.6 Welded Beam Design Problem Case 2
A.2.7 Multiple Disc Clutch Brake
A.2.8 Helical Compression Spring Design
A.2.9 Minimise I-Beam Vertical Deflection
A.2.10 Cantilever Beam
A.2.11 The Gear Train Design Problem
A.3 Test Functions
A.3.1 Problem 3: Integer Linear Programming
A.3.2 Problem 4: Rosen–Suzuki Test Problem Convex Programming Problem
A.3.3 Dynamic Variable Problem
A.3.4 Transportation Problem
A.3.5 Multistage Problem
A.3.6 Knapsack Problem
A.3.7 Integer Linear Programming
A.3.8 Non-Convex Integer Problem
A.3.9 Global Nonlinear Programming
A.3.10 3-Bar Truss Structure
A.4 Real-World Application From Manufacturing Domain
A.4.1 Multi Pass Turning Process
A.4.2 Multi-Pass Milling Process Problem
References
Index