Computational Optimization, Methods and Algorithms

Title
Preface
Contents
Computational Optimization: An Overview
	Introduction
	Computational Optimization
	Optimization Procedure
	Optimizer
		Optimization Algorithms
		Choice of Algorithms
	Simulator
		Numerical Solvers
		Simulation Efficiency
	Latest Developments
	References
Optimization Algorithms
	Introduction
	Derivative-Based Algorithms
		Newton's Method and Hill-Climbing
		Conjugate Gradient Method
	Derivative-Free Algorithms
		Pattern Search
		Trust-Region Method
	Metaheuristic Algorithms
		Simulated Annealling
		Genetic Algorithms and Differential Evolution
		Particle Swarm Optimization
		Harmony Search
		Firefly Algorithm
		Cuckoo Search
	A Unified Approach to Metaheuristics
		Characteristics of Metaheuristics
	Generalized Evolutionary Walk Algorithm (GEWA)
		To Be Inspired or Not to Be Inspired
	References
Surrogate-Based Methods
	Introduction
	Surrogate-Based Optimization
	Surrogate Models
		Design of Experiments
		Surrogate Modeling Techniques
		Model Validation
		Surrogate Correction
	Surrogate-Based Optimization Techniques
		Approximation Model Management Optimization
		Space Mapping
		Manifold Mapping
		Surrogate Management Framework
		Exploitation versus Exploration
	Final Remarks
	References
Derivative-Free Optimization
	Introduction
	Derivative-Free Optimization
	Local Optimization
		Pattern Search Methods
		Derivative-Free Optimization with Interpolation and Approximation Models
	Global Optimization
		Evolutionary Algorithms
		Estimation of Distribution Algorithms
		Particle Swarm Optimization
		Differential Evolution
	Guidelines for Generally Constrained Optimization
		Penalty Functions
		Augmented Lagrangian Method
		Filter Method
		Other Approaches
	Concluding Remarks
	References
Maximum Simulated Likelihood Estimation: Techniques and Applications in Economics
	Introduction
	Copula Model
	Estimation Methodology
		The CRT Method
		Optimization Technique
	Application
	Concluding Remarks
	References
Optimizing Complex Multi-location Inventory Models Using Particle Swarm Optimization
	Introduction
	RelatedWork
	Simulation Optimization
	Multi-Location Inventory Models with Lateral Transshipments
		Features of a General Model
		Features of the Simulation Model
	Particle Swarm Optimization
	Experimentation
		System Setup
		Results and Discussion
	Conclusion and Future Work
	References
Traditional and Hybrid Derivative-Free Optimization Approaches for Black Box Functions
	Introduction and Motivation
	A Motivating Example
	Some Traditional Derivative-Free Optimization Methods
		Genetic Algorithms (GAs)
		Deterministic Sampling Methods
		Statistical Emulation
	Some DFO Hybrids
		APPS-TGP
		EAGLS
		DIRECT-IFFCO
		DIRECT-TGP
	Summary and Conclusion
	References
Simulation-Driven Design in Microwave Engineering: Methods
	Introduction
	Direct Approaches
	Surrogate-Based Design Optimization
	Surrogate Models for Microwave Engineering
	Microwave Simulation-Driven Design Exploiting Physically-Based Surrogates
		Space Mapping
		Simulation-Based Tuning and Tuning Space Mapping
		Shape-Preserving Response Prediction
		Multi-fidelity Optimization Using Coarse-Discretization EM Models
		Optimization Using Adaptively Adjusted Design Specifications
	Summary
	References
Variable-Fidelity Aerodynamic Shape Optimization
	Introduction
	Problem Formulation
	Computational Fluid Dynamic Modeling
		Governing Equations
		Numerical Modeling
	Direct Optimization
		Gradient-Based Methods
		Derivative-Free Methods
	Surrogate-Based Optimization
		The Concept
		Surrogate Modeling
		Optimization Techniques
	Summary
	References
Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
	Introduction
	Basic Concepts
		Pareto Dominance
		Pareto Optimality
		Pareto Front
	Multi-Objective Aerodynamic Shape Optimization
		Problem Definition
		Surrogate-Based Optimization
		Hybrid MOEA Optimization
		Robust Design Optimization
		Multi-Disciplinary Design Optimization
		Data Mining and Knowledge Extraction
	A Case Study
		Objective Functions
		Geometry Parameterization
		Constraints
		Evolutionary Algorithm
		Results
	Conclusions and Final Remarks
	References
An Enhanced Support Vector Machines Model for Classification and Rule Generation
	Basic Concept of Classification and Support Vector Machines
	Data Preprocessing
		Data Cleaning
		Data Transformation
		Data Reduction
	Parameter Determination of Support Vector Machines by Meta-heuristics
		Genetic Algorithm
		Immune Algorithm
		Particle Swarm Optimization
	Rule Extraction Form Support Vector Machines
	The Proposed Enhanced SVM Model
	A Numerical Example and Empirical Results
	Conclusion
	References
Benchmark Problems in Structural Optimization
	Introduction to Benchmark Structural Design
		Structural Engineering Design and Optimization
	Classifications of Benchmarks
	Design Benchmarks
		Truss Design Problems
		Non-truss Design Problems
	Discussions and Further Research
	References
Author Index