Computational Optimization, Methods andAlgorithms

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 Related Work 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 Cover Front Matter Computational Optimization: An Overview Introduction Computational Optimization Optimization Procedure Optimizer Optimization Algorithms Choice of Algorithms Simulator Numerical Solvers Simulation Efficiency References Latest Developments 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) References To Be Inspired or Not to Be Inspired Surrogate-Based Methods* Introduction Surrogate-Based Optimization Surrogate Models Design of Experiments Surrogate Modeling Techniques Surrogate Correction Model Validation Surrogate-Based Optimization Techniques Space Mapping Approximation Model Management Optimization Manifold Mapping Surrogate Management Framework Final Remarks Exploitation versus Exploration 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 Related Work 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-TGP DIRECT-IFFCO 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 Surrogate-Based Optimization Derivative-Free Methods The Concept Surrogate Modeling Optimization Techniques Summary References Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization Introduction Basic Concepts Pareto Dominance Pareto Optimality Multi-Objective Aerodynamic Shape Optimization Problem Definition Pareto Front 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 Evolutionary Algorithm Constraints 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 Back Matter