Xin-She Yang

Slawomir Koziel

Contemporary design in engineering and industry relies heavily on computer simulation and efficient algorithms to reduce the cost and to maximize the performance and sustainability as well as profits and energy efficiency. Solving an optimization problem correctly and efficiently requires not only the right choice of optimization algorithms and simulation methods, but also the proper implementation and insight into the problem of interest. This book consists of ten self-contained, detailed case studies of real-world optimization problems, selected from a wide range of applications and contributed from worldwide experts who are working in these exciting areas. Optimization topics and applications include gas and water supply networks, oil field production optimization, microwave engineering, aerodynamic shape design, environmental emergence modelling, structural engineering, waveform design for radar and communication systems, parameter estimation in laser experiment and measurement, engineering materials and network scheduling. These case studies have been solved using a wide range of optimization techniques, including particle swarm optimization, genetic algorithms, artificial bee colony, harmony search, adaptive error control, derivative-free pattern search, surrogate-based optimization, variable-fidelity modelling, as well as various other methods and approaches. This book is a practical guide to help graduates and researchers to carry out optimization for real-world applications. More advanced readers will also find it a helpful reference and aide memoire.

Computational Optimization and Applications in Engineering and Industry

Qi-Jun Zhang

Computer-aided full-wave electromagnetic (EM) analysis has been used in microwave engineering for the past decade. Initially, its main application area was design verification. Today, EM-simulation-driven optimization and design closure become increasingly important due to the complexity of microwave structures and increasing demands for accuracy. In many situations, theoretical models of microwave structures can only be used to yield the initial designs that need to be further fine-tuned to meet given performance requirements. In addition, EM-based design is a must for a growing number of microwave devices such as ultra-wideband (UWB) antennas, dielectric resonator antennas and substrate-integrated circuits. For circuits like these, no design-ready theoretical models are available, so design improvement can only be obtained through geometry adjustments based on repetitive, time-consuming simulations. On the other hand, various interactions between microwave devices and their environment, such as feeding structures and housing, must be taken into account, and this is only possible through full-wave EM analysis. Electromagnetic simulations can be highly accurate, but they tend to be computationally expensive. Therefore, practical design optimization methods have to be computationally efficient, so that the number of CPU-intensive high-fidelity EM simulations is reduced as much as possible during the design process. For the same reasons, techniques for creating fast yet accurate models of microwave structures become crucially important. In this edited book, the authors strive to review the state-of-the-art simulation-driven microwave design optimization and modeling. A group of international experts specialized in various aspects of microwave computer-aided design summarize and review a wide range of the latest developments and real-world applications. Topics include conventional and surrogate-based design optimization techniques, methods exploiting adjoint sensitivity, simulation-based tuning, space mapping, and several modeling methodologies, such as artificial neural networks and kriging. Applications and case studies include microwave filters, antennas, substrate integrated structures and various active components and circuits. The book also contains a few introductory chapters highlighting the fundamentals of optimization and modeling, gradient-based and derivative-free algorithms, metaheuristics, and surrogate-based optimization techniques, as well as finite difference and finite element methods. Contents:Introduction to Optimization and Gradient-Based Methods (Xin-She Yang and Slawomir Koziel)Derivative-Free Methods and Metaheuristics (Xin-She Yang and Slawomir Koziel)Surrogate-Based Optimization (Slawomir Koziel, Leifur Leifsson, and Xin-She Yang)Space Mapping (Slawomir Koziel, Stanislav Ogurtsov, Qingsha S Cheng, and John W Bandler)Tuning Space Mapping (Qingsha S Cheng, John W Bandler, and Slawomir Koziel)Robust Design Using Knowledge-Based Response Correction and Adaptive Design Specifications (Slawomir Koziel, Stanislav Ogurtsov, and Leifur Leifsson)Simulation-Driven Design of Broadband Antennas Using Surrogate-Based Optimization (Slawomir Koziel and Stanislav Ogurtsov)Neural Networks for Radio Frequency/Microwave Modeling (Chuan Zhang, Lei Zhang, and Qi-Jun Zhang)Parametric Modeling of Microwave Passive Components Using Combined Neural Network and Transfer Function (Yazi Cao, Venu-Madhav-Reddy Gongal-Reddy, and Qi-Jun Zhang)Parametric Sensitivity Macromodels for Gradient-Based Optimization (Krishnan Chemmangat, Francesco Ferranti, Tom Dhaene, and Luc Knockaert)Neural Space Mapping Methods for Electromagnetics-Based Yield Estimation (José E Rayas-Sánchez)Neural Network Inverse Modeling for Microwave Filter Design (Humayun Kabir, Ying Wang, Ming Yu, and Qi-Jun Zhang)Simulation-Driven Design of Microwave Filters for Space Applications (Elena Díaz Caballero, José Vicente Morro Ros, Héctor Esteban González, Vicente Enrique Bôria Esbert, Carmen Bachiller Martín, and Ángel Belenguer Martinez)Time Domain Adjoint Sensitivities: The Transmission Line Modeling (TLM) Case (Mohamed H Bakr and Osman S Ahmed)Boundary Conditions for Two-Dimensional Finite-Element Modeling of Microwave Devices (Tian-Hong Loh and Christos Mias)Boundary Conditions for Three-Dimensional Finite-Element Modeling of Microwave Devices (Tian-Hong Loh and Christos Mias) Readership: Graduates, lecturers, and researchers in electrical engineering, as well as engineers who use numerical optimization in their design work. This book will be of great interest to researchers in the fields of microwave engineering, antenna design, and computational electromagnetics. Keywords:Computer-Aided Design;Electromagnetic Simulation;Microwave Design;Numerical Optimization;Surrogate ModelingKey Features:This book summarizes the latest developments in the fieldIt provides a balanced coverage of classical and engineering-oriented optimization methods in one volume, and also includes methodologies not covered by any other book elsewhere, such as robust modeling methodologies (both conventional and modern) and physically-based approaches; surrogate-based techniques and microwave-engineering specific approaches simulation-driven design methods for computationally expensive problemsThis book covers both introductory materials, practical methods and algorithms, as well as applications and case studies

Simulation-Driven Design Optimization and Modeling for Microwave Engineering

Leifur Leifsson

Contemporary engineering design is heavily based on computer simulations. Accurate, high-fidelity simulations are used not only for design verification but, even more importantly, to adjust parameters of the system to have it meet given performance requirements. Unfortunately, accurate simulations are often computationally very expensive with evaluation times as long as hours or even days per design, making design automation using conventional methods impractical. These and other problems can be alleviated by the development and employment of so-called surrogates that reliably represent the expensive, simulation-based model of the system or device of interest but they are much more reasonable and analytically tractable. This volume features surrogate-based modeling and optimization techniques, and their applications for solving difficult and computationally expensive engineering design problems. It begins by presenting the basic concepts and formulations of the surrogate-based modeling and optimization paradigm and then discusses relevant modeling techniques, optimization algorithms and design procedures, as well as state-of-the-art developments. The chapters are self-contained with basic concepts and formulations along with applications and examples. The book will be useful to researchers in engineering and mathematics, in particular those who employ computationally heavy simulations in their design work.

Applications in Engineering

Surrogate-Based Modeling and Optimization

Adrian Bekasiewicz

This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods. Based on contemporary research, it formulates multi-objective design tasks, highlights related challenges in the context of antenna design, and discusses solution approaches. Specific focus is on providing methodologies for handling computationally expensive simulation models of antenna structures in the sense of their multi-objective optimization. Also given is a summary of recent developments in antenna design optimization using variable-fidelity simulation models. Numerous examples of real-world antenna design problems are provided along with discussions and recommendations for the readers interested in applying the considered methods in their design work.Written with researchers and students in mind, topics covered can also be applied across a broad spectrum of aeronautical, mechanical, electrical, biomedical and civil engineering. It is of particular interest to those dealing with optimization, computationally expensive design tasks and simulation-driven design.

Multi-objective Design Of Antennas Using Surrogate Models

Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry. This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry.

Computational Optimization, Methods and Algorithms

Computer simulations is a fundamental tool of the design process in many engineering disciplines including aerospace engineering. However, although high-fidelity numerical models are accurate, they can be computationally expensive with evaluation time for a single design as long as hours, days or even weeks. Simulation-driven design using conventional optimization techniques may be therefore prohibitive. This book explores the alternative: performing computationally efficient design using surrogate-based optimization, where the high-fidelity model is replaced by its computationally cheap but still reasonably accurate representation: a surrogate. The emphasis is on physics-based surrogates. Application-wise, the focus is on aerodynamics and the methods and techniques described in the book are demonstrated using aerodynamic shape optimization cases. Applications in other engineering fields are also demonstrated. State-of-the-art techniques and a depth of coverage never published before make this a unique and essential book for all researchers working in aerospace and other engineering areas and dealing with optimization, computationally expensive design problems, and simulation-driven design. Contents:Motivation and Problem Formulation:IntroductionAerodynamic Shape OptimizationOptimization Techniques:Simulation-Driven Design: Direct MethodsSurrogate-Based OptimizationSBO with Approximation-Based SurrogatesSBO with Physics-Based SurrogatesAerodynamics Modeling:Geometry ParameterizationHigh-Fidelity Aerodynamic ModelsLow-Fidelity Aerodynamics ModelsApplications:Transonic Airfoil Shape DesignTransonic Wing Shape DesignSubsonic Shape DesignSelected Applications of Surrogate-Based Optimization in Other AreasSurrogate-Based Optimization with MATLABConclusion:Practical Aspects of Variable-Fidelity Design Readership: Graduate students and researchers in the field of engineering, in particular, aerospace engineering. Key Features:Gathers a number of relevant techniques that were never compiled in one publication before, and certain state-of-the-art techniques have never been published in book formCompact and self-contained introduction to the area of surrogate-based optimization and variable-fidelity optimizationAt present, this is the only book available on the market that offers coverage of variable-fidelity optimization methodsKeywords:Aerodynamic Shape Optimization;Computational Fluid Dynamics (CFD);Surrogate Modeling;Surrogate-based Optimization;Variable-fidelity Simulations;Simulation-driven Design

Slawomir Koziel

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