Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications (Springer Tracts in Nature-Inspired Computing)

✍ Scribed by Serdar Carbas (editor), Abdurrahim Toktas (editor), Deniz Ustun (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 420
• Category: Library

✦ Synopsis

This book engages in an ongoing topic, such as the implementation of nature-inspired metaheuristic algorithms, with a main concentration on optimization problems in different fields of engineering optimization applications. The chapters of the book provide concise overviews of various nature-inspired metaheuristic algorithms, defining their profits in obtaining the optimal solutions of tiresome engineering design problems that cannot be efficiently resolved via conventional mathematical-based techniques. Thus, the chapters report on advanced studies on the applications of not only the traditional, but also the contemporary certain nature-inspired metaheuristic algorithms to specific engineering optimization problems with single and multi-objectives. Harmony search, artificial bee colony, teaching learning-based optimization, electrostatic discharge, grasshopper, backtracking search, and interactive search are just some of the methods exhibited and consulted step by step in applicationcontexts. The book is a perfect guide for graduate students, researchers, academicians, and professionals willing to use metaheuristic algorithms in engineering optimization applications.

✦ Table of Contents

Preface
Contents
Editors and Contributors
About the Editors
Contributors
1 Introduction and Overview: Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications
1.1 Introduction
1.2 Parts
1.2.1 Part I: Civil and Structural Engineering
1.2.2 Part II: Electrical and Electronics, Computer, and Communication Engineering
1.3 Concluding Remarks
References
Part I Civil and Structural Engineering
2 Harmony Search Algorithm for Structural Engineering Problems
2.1 Introduction
2.2 Metaheuristics and Harmony Search
2.2.1 Mathematical Representation of Engineering Optimization Problems
2.2.2 Harmony Search (HS)
2.3 Survey on Applications in Structural Engineering
2.3.1 Steel Structures
2.3.2 Reinforced Concrete (RC) Structures
2.3.3 Structural Control
2.3.4 Others
2.4 The Optimization Problems
2.4.1 Optimization of Design Variables for CFRP Used for Increasing the Shear Force Capacity of RC Beams
2.4.2 Optimization of Design Variables for I-Beam Vertical Deflection Minimization
2.5 Conclusions
Appendix
References
3 Teaching Learning Based Optimum Design of Transmission Tower Structures
3.1 Introduction
3.2 Optimum Design Problem
3.3 Teaching Learning Based Optimization (TLBO)
3.4 Design Examples
3.4.1 47-Member Plane Transmission Tower
3.4.2 72-Member Space Transmission Tower
3.4.3 244-Member Space Transmission Tower
3.5 Conclusions
References
4 Modified Artificial Bee Colony Algorithm for Sizing Optimization of Truss Structures
4.1 Introduction
4.2 Formulation of the Truss Optimization Problem
4.3 Artificial Bee Colony Algorithm (ABC)
4.4 Modified Artificial Bee Colony Algorithm (MABC)
4.5 Truss Sizing Optimization with the MABC
4.6 Design Examples
4.6.1 Planar Ten-Bar Truss
4.6.2 Spatial Twenty-Five Bar Truss
4.6.3 Spatial Seventy-Two Bar Truss
4.6.4 Planar Two-Hundred Bar Truss
4.7 Concluding Remarks
References
5 Electrostatic Discharge Algorithm for Optimum Design of Real-Size Truss Structures
5.1 Introduction
5.2 Discrete Optimization Problem Formulation of Truss Structures
5.2.1 Penalty Function and Penalized Objective Function
5.3 Electrostatic Discharge Algorithm (ESDA)
5.3.1 Electrostatic Discharge (ESD)
5.3.2 Interpretation of the ESD Algorithm
5.3.3 Determination of Search Parameters of ESDA
5.4 Design Examples
5.4.1 160-Bar Steel Truss Pyramid
5.4.2 1032-Bar Double-Layer Steel Truss Roof Structure
5.5 Conclusions
References
6 Solving of Distinct Engineering Optimization Problems Using Metaheuristic Algorithms
6.1 Introduction
6.2 The Optimization Methods Employed in the Current Chapter
6.2.1 Firefly Algorithm (FA)
6.2.2 Teaching and Learning-Based Optimization (TLBO)
6.2.3 Drosophila Food-Search Optimization (DSO)
6.2.4 Interactive Search Algorithm (ISA)
6.2.5 Butterfly Optimization Algorithm (BOA)
6.3 Numerical Examples
6.3.1 Mathematical Functions
6.3.2 Mechanical Problems
6.3.3 Structural Design Problem
6.3.4 Project Management Problem
6.4 Conclusions
References
7 The Design of Trapezoidal Corrugated Web Beams Using Firefly Method
7.1 Introduction
7.2 Design of Trapezoidal Corrugated Web Beam
7.2.1 Yielding Capacity of Trapezoidal Web Beams
7.2.2 Local Buckling Capacity of Flanges
7.2.3 Global Buckling Capacity of Flanges
7.3 Firefly Optimization Method
7.4 Benchmark Minimization Design Example
7.5 Benchmark Maximization Design Example
7.6 Design of Corrugated Beam
7.7 Optimum Design Problem of Trapezoidal Web Beam
7.8 Conclusions
References
8 Designing Fuzzy Controllers for Frame Structures Based on Ground Motion Prediction Using Grasshopper Optimization Algorithm: A Case Study of Tabriz, Iran
8.1 Introduction
8.2 Ground Motion Prediction
8.3 Fuzzy Logic Controller
8.4 Grasshopper Optimization Algorithm (GOA)
8.5 Design Example
8.6 Statement of the Optimization Problem
8.7 Numerical Results
8.8 Conclusions
References
9 Optimization and Artificial Neural Network Models for Reinforced Concrete Members
9.1 Introduction
9.2 Review of AI and Machine Learning Applications for Structural Optimization
9.3 Artificial Neural Networks (ANNs)
9.4 Metaheuristic Algorithms and Optimization
9.4.1 Teaching–Learning-Based Optimization (TLBO)
9.4.2 Jaya Algorithm (JA)
9.5 Machine Learning Applications via ANNs for Reinforced Concrete (RC) Structures
9.5.1 T-Shaped RC Beam
9.5.2 Beam with Carbon Fiber Reinforced Polymer (CFRP)
9.6 Conclusions
References
10 Statistical Investigation of the Robustness for the Optimization Algorithms
10.1 Introduction
10.2 Optimization Analysis via Scatter Search
10.2.1 Scatter Search
10.2.2 The Optimum Design of the Cantilever Retaining Wall
10.3 Taguchi Method and Implementation of the SS Algorithm to the CRW Design
10.3.1 Taguchi Method
10.3.2 Implementation of SS Algorithm to the CRW Design
10.4 Analysis Results
10.4.1 Statistical Analysis via L16 Design Table
10.4.2 Statistical Analysis via L9 Design Table
10.5 Conclusions
References
11 Optimum Design of Beams with Varying Cross-Section by Using Application Interface
11.1 Introduction
11.2 Optimization
11.2.1 Harmony Search Algorithm (HSA)
11.2.2 Backtracking Search Optimization Algorithm (BSA)
11.2.3 Constraint Handling
11.2.4 Discrete Design Variables
11.2.5 Programming Application Interfaces
11.3 Problem Definition and Results
11.3.1 Three-Bar Truss Design Problem
11.3.2 Beams with Varying Cross-Section
11.4 Conclusions
References
12 Metaheuristic-Based Structural Control Methods and Comparison of Applications
12.1 Introduction
12.2 Review of Recent Structural Control Applications Using Metaheuristics
12.2.1 Tuned Mass Dampers
12.2.2 Active Tendon Control
12.3 Equations of Motion and Optimization Methodologies
12.3.1 TMD and ATMD
12.3.2 Active Tendon Control
12.3.3 Proportional–Integral–Derivative Controller
12.3.4 Metaheuristic-Based Optimization
12.4 Numerical Examples Comparing ATMD and Active Tendons
12.5 Conclusions and Future Studies
References
13 Evolutionary Structural Optimization—A Trial Review
13.1 Introduction
13.2 Structural Optimization Concept
13.3 Topology Optimization Methodology
13.4 Keystones of the Algorithm
13.5 Basic Principles
13.6 Objectives and Constraints
13.7 Optimization Parameters
13.7.1 Rejection and Evolutionary Rates
13.7.2 Element Removal Ratio
13.7.3 Element Size
13.8 Optimality Decision
13.9 Advances of the Algorithm
13.9.1 Multi-loading and Multi-support Conditions
13.9.2 Multi-criteria Utilization
13.9.3 Bidirectional Optimization
13.9.4 Grouping Algorithm
13.9.5 Morphing Algorithm
13.9.6 Combination with Strut-and-Tie Method
13.9.7 Combination with Other Metaheuristic Algorithms
13.10 Superiorities of the Algorithm
13.11 Conclusions
References
14 An Extensive Review of Charged System Search Algorithm for Engineering Optimization Applications
14.1 Introduction
14.2 General Formulation of CSS
14.2.1 Inspiration
14.2.2 Mathematical Model
14.2.3 Implementation of the CSS
14.3 Applications of CSS
14.3.1 Applications to Structural Engineering Design
14.3.2 Applications on Control Systems
14.3.3 Applications on Damage Detection
14.3.4 Applications on Robotics and Power Systems
14.3.5 Applications on Other Optimization Problems
14.4 Modifications of CSS
14.5 Hybridizations of CSS
14.6 Multi-Objective CSS Approaches
14.7 Conclusion
References
Part II Electrical and Electronics, Computer, and Communication Engineering
15 Artificial Bee Colony Algorithm and Its Application to Content Filtering in Digital Communication
15.1 Introduction
15.2 Foraging in a Real Honey Bee Colony
15.3 Artificial Bee Colony Algorithm
15.3.1 Initialization
15.3.2 Employed Bee Phase
15.3.3 Onlooker Bee Phase
15.3.4 Scout Bee Phase
15.4 How the ABC Algorithm Evolves Food Sources
15.5 An Application of the Artificial Bee Colony Algorithm to Content Filtering in Digital Communication
15.5.1 Problem Description
15.5.2 Logistic Regression
15.5.3 ABC-Based LR Classifier
15.5.4 Feature Representation and Selection
15.5.5 Experimental Settings
15.5.6 Results
15.6 Conclusion
References
16 Multi-objective Design of Multilayer Microwave Dielectric Filters Using Artificial Bee Colony Algorithm
16.1 Introduction
16.2 MO-ABC Algorithm
16.2.1 Pareto Optimality Algorithm
16.2.2 ABC Algorithm
16.3 Multi-objective EM Model of the MMDF
16.3.1 The Dual-Objective Functions for the Design of MMDFs
16.4 The Designed MMDFs Through MO-ABC
16.4.1 The Set Parameters and Material Database
16.4.2 The Performance Results of the Designed MMDFs
16.5 Conclusions
References
17 Multi-objective Sparse Signal Reconstruction in Compressed Sensing
17.1 Introduction
17.2 Multi-objective Optimization
17.3 Compressed Sensing
17.4 Multi-objective Sparse Reconstruction
17.4.1 ECG Signal Compression
17.5 Conclusion
References
18 Optimal Allocation of Flexible Alternative Current Transmission Systems: An Application of Particle Swarm Optimization
18.1 Introduction
18.2 Distribution Voltage Regulation and Its Issue
18.3 Target Optimization Problem
18.4 Particle Swarm Optimization-Based Solution Method
18.4.1 Particle Swarm Optimization
18.4.2 Improved Particle Swarm Optimization (RAPSO-ME)
18.4.3 Validation of Improved Particle Swarm Optimization
18.5 Numerical Simulation and Discussion on Its Result
18.6 Conclusions
References