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

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