The main purpose of the present book is to develop a general framework for population-based metaheuristics based on some basic concepts of set theory. The idea of the framework is to divide the population of individuals into subpopulations of identical sizes. Therefore, in each iteration of the search process, different subpopulations explore the search space independently but simultaneously. The framework aims to provide a suitable balance between exploration and exploitation during the search process. A few chapters containing algorithm-specific modifications of some state-of-the-art metaheuristics are also included to further enrich the book.
The present book is addressed to those scientists, engineers, and students who wish to explore the potentials of newly developed metaheuristics. The proposed metaheuristics are not only applicable to structural optimization problems but can also be used for other engineering optimization applications. The book is likely to be of interest to a wide range of engineers and students who deal with engineering optimization problems.
Author(s): Ali Kaveh, Kiarash Biabani Hamedani
Series: Studies in Computational Intelligence, 1059
Publisher: Springer
Year: 2022
Language: English
Pages: 368
City: Cham
Preface
Contents
1 Introduction
1.1 Introduction
1.2 Organization of the Present Book
References
2 Set-Theoretical Shuffled Shepherd Optimization Algorithm for Optimal Design of Reinforced Concrete Cantilever Retaining Wall Structures
2.1 Introduction
2.2 Shuffled Shepherd Optimization Algorithm (SSOA)
2.3 Set-Theoretical Shuffled Shepherd Optimization Algorithm (ST-SSOA)
2.4 Definition of the Optimization Problem
2.5 Analysis of Cantilever Retaining Walls
2.5.1 Active and Passive Earth Pressure Coefficients
2.5.2 Stability Analysis of Cantilever Retaining Walls
2.6 Results and Discussion
2.7 Concluding Remarks
References
3 Set-Theoretical Variants of the Teaching–Learning-Based Optimization Algorithm for Structural Optimization with Frequency Constraints
3.1 Introduction
3.2 Teaching–Learning-Based Optimization (TLBO) Algorithm
3.3 Set-Theoretical Variants of the Teaching–Learning-Based Optimization Algorithm
3.3.1 Ordered Set-Theoretical Teaching–Learning-Based Optimization (OST-TLBO) Algorithm
3.3.2 Set-Theoretical Multi-Phase Teaching–Learning-Based Optimization (STMP-TLBO) Algorithm
3.4 Formulation of Truss Optimization Problem with Frequency Constraints
3.5 Numerical Examples
3.5.1 A 37-Bar Planar Truss
3.5.2 A 52-Bar Dome Truss
3.5.3 A 120-Bar Dome Truss
3.5.4 A 200-Bar Planar Truss
3.6 Concluding Remarks
References
4 Enhanced Set-Theoretical Versions of the Shuffled Shepherd Optimization Algorithm for Structural Optimization
4.1 Introduction
4.2 Overview of the Shuffled Shepherd Optimization Algorithm (SSOA)
4.3 Parameter-Free Shuffled Shepherd Optimization Algorithm (PF-SSOA)
4.4 Set-Theoretical Multi-phase Shuffled Shepherd Optimization Algorithm (STMP-SSOA)
4.5 Formulation of the Optimization Problems
4.5.1 Size Optimization of Truss Structures with Frequency Constraints
4.5.2 Discrete Size Optimization of Steel Frame Structures
4.6 Numerical Examples
4.6.1 A 120-Bar Dome-Like Truss
4.6.2 A 200-Bar Planar Truss
4.6.3 A 3-Bay 15-Story Steel Frame Structure
4.6.4 A 3-Bay 24-Story Steel Frame Structure
4.7 Concluding Remarks
References
5 Set-Theoretical Metaheuristic Algorithms for Reliability-Based Design Optimization of Truss Structures
5.1 Introduction
5.2 Set-Theoretical Variants of the Population-Based Optimization Algorithms
5.2.1 Set-Theoretical Variants of the Teaching–Learning-Based Optimization Algorithm
5.2.2 Set-Theoretical Variant of the Shuffled Shepherd Optimization Algorithm
5.2.3 Set-Theoretical Variant of the Jaya Algorithm
5.3 System Reliability Analysis of Truss Structures
5.3.1 Generation of Safety Margins for Truss Structures
5.3.2 The Branch and Bound Method
5.3.3 Evaluation of the System Reliability
5.4 System Reliability-Based Design Optimization of Truss Structures
5.5 Numerical Examples
5.5.1 Statically Indeterminate 16-Member Planar Truss
5.5.2 Statically Indeterminate 65-Member Truss Bridge
5.5.3 Statically Indeterminate 67-Member Truss Bridge
5.6 Concluding Remarks
References
6 Optimal Analysis in the Service of Frequency-Constrained Structural Optimization with Set-Theoretical Jaya Algorithm
6.1 Introduction
6.2 Free Vibration Analysis of Structures
6.3 Efficient Free Vibration Analysis of Cyclic Symmetric Structures
6.3.1 Structural Matrices in the Cartesian and Cylindrical Coordinate Systems
6.3.2 Efficient Eigensolution Method for Free Vibration Analysis of Cyclic Symmetric Structures
6.4 Mathematical Formulation of the Optimization Problem
6.5 Optimization Algorithms
6.5.1 Jaya Algorithm
6.5.2 Set-Theoretical Jaya Algorithm
6.6 Results and Discussion
6.6.1 A 600-Bar Single-Layer Dome-Like Truss
6.6.2 A 1410-Bar Double-Layer Dome-Like Truss
6.7 Concluding Remarks
References
7 Discrete Structural Optimization with Set-Theoretical Jaya Algorithm
7.1 Introduction
7.2 Structural Optimization with Discrete Design Variables
7.3 Classical Jaya Algorithm (JA)
7.4 Set-Theoretical Jaya Algorithm (ST-JA)
7.5 Numerical Examples
7.5.1 A 72-Bar Spatial Truss Structure
7.5.2 A 47-Bar Planar Power Line Tower
7.5.3 A 52-Bar Planar Truss Structure
7.5.4 A 160-Bar Spatial Truss Structure
7.6 Concluding Remarks
References
8 Enhanced Forensic-Based Investigation Algorithm
8.1 Introduction
8.2 Forensic-Based Investigation (FBI) Algorithm
8.2.1 Forensic Investigation Process
8.2.2 Mathematical Model
8.3 Enhanced Forensic-Based Investigation (EFBI)
8.4 Formulation of the Optimization Problem
8.5 Numerical Examples
8.5.1 A 52-Bar Dome-Like Truss
8.5.2 A 120-Bar Dome-Like Truss
8.5.3 A 600-Bar Dome-Like Truss
8.6 Concluding Remarks
References
9 Improved Slime Mould Algorithm
9.1 Introduction
9.2 Overview of the Slime Mould Algorithm (SMA)
9.3 Proposed Improved Slime Mould Algorithm (ISMA)
9.4 Formulation of the Optimization Problem
9.5 Numerical Examples
9.5.1 A 600-Bar Dome-Like Truss
9.5.2 A 1180-Bar Dome-Like Truss
9.5.3 A 1410-Bar Dome-Like Truss
9.6 Concluding Remarks
References
10 Improved Arithmetic Optimization Algorithm
10.1 Introduction
10.2 Overview of the Arithmetic Optimization Algorithm (AOA)
10.2.1 Initialization Phase
10.2.2 Exploration Phase
10.2.3 Exploitation Phase
10.3 Proposed Improved Arithmetic Optimization Algorithm (IAOA)
10.4 Structural Optimization with Discrete Design Variables
10.5 Numerical Examples
10.5.1 A 72-Bar Space Truss
10.5.2 A 384-Bar Double-Layer Barrel Vault
10.5.3 A 3-Bay 15-Story Steel Frame
10.6 Application to High-Dimensional Structural Optimization Problems
10.7 Concluding Remarks
References
