Multi-objective design of antennas using surrogate models pdf

Multi-objective design of antennas using surrogate models pdf
Multi-Objective Design of Antennas Using Surrogate Models eBook: Slawomir Koziel, Adrian Bekasiewicz, Rolf Binder: Amazon.co.uk: Kindle Store
A case study applying the previous version of the process design model and the revised version (with rate based model introduced as a set of five surrogate models) will demonstrate that the prediction uncertainties of the process design & optimisation methodology are reduced due to the integration of the rate based model of the fluidised bed methanation reactor. It will be shown that the
trade-o solutions for multi-objective optimization problems using surrogate models. The The literature on surrogate modeling for constrained optimization problems is also rare.
Multi-Objective Airfoil Shape Optimization Using a Multiple-Surrogate Approach We will limit our discussion to the use of surrogate models in multi-objective ASO problems, since that is the scope of this paper. In this context, surrogate models [4] replace direct calls to any CFD simulation code. Figure 1 shows the general flow in any surrogate-based optimization approach. When designing
Abstract: A computationally-efficient procedure for multi-objective design of antennas is presented. Our approach is general; however, in the particular case study considered here, the goal is to improve the antenna gain while ensuring that the matching requirements are satisfied.
A procedure for low-cost multi-objective design optimization of antenna structures is discussed. The major stages of the optimization process include: (i) an initial reduction of the search space aimed at identifying its relevant subset containing the Pareto-optimal design space, (ii) construction—using sampled coarse-discretization
This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods.
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In this chapter, we present a multi-objective design procedure for antennas that allows us to obtain a Pareto front, i.e., multiple designs representing the trade-off between various characteristics of the antenna under consideration.

—In this paper we focus on multi-objective optimization in electromagnetic problems with given priorities among the targets. The approach proposed in this paper is able to build a proper cost function capable to correctly implementing the design
A generic approach to construct surrogate models of antennas involves the use of coarse-discretisation EM simulations (low-fidelity models). A proper selection of the surrogate model …
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
Originality/value– Multi-objective design of antenna structures is very challenging when high-fidelity electromagnetic simulations are utilized for performance evaluation of the structure at hand. The proposed design framework permits rapid optimization of complex structures (here, MIMO antenna), which is hardly possible using conventional methods.

Simulation-Based Multi-objective Antenna Optimization with

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Scalability of surrogate-assisted multi-objective

regression models), that are created in order to reduce the dependency on finite element (FE-)simulations during the multi-objective optimization run, can be easily reused to perform very fast local and global tolerance/sensitivity analyses of generated designs.
There is also a discussion of the selection of antenna model fidelity and its influence on performance of the surrogate-based design process. This volume is suitable for electrical engineers in academia as well as industry, antenna designers and engineers dealing with computationally-expensive design …
Using Multi-Objective Evolutionary Algorithms (MOEAs) and genetic algorithms [1,2,3,4,5,6] is not desirable, since they typically require a large number of objective function evaluations during the optimization process.
Design of the series fed microstrip patch planar array antenna by the parato genetic algorithm @article{Tsutsumi2015DesignOT, title={Design of the series fed microstrip patch planar array antenna by the parato genetic algorithm}, author={Hiroki Tsutsumi and Yoshihiko Kuwahara and Hiroyuki Kamo}, journal={2015 IEEE International Symposium on
Multi-Objective Design of Antennas Using Surrogate Models, Slawomir Koziel, Adrian Bekasiewicz, WSPC (EUROPE). Des milliers de livres avec la livraison chez vous en 1 jour ou en magasin avec …
Multi-Objective Design Exploration for Aerodynamic Configurations Shigeru Obayashi*, A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization problems. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists …
Design/methodology/approach – The task of identifying the best possible trade-offs between the antenna size and its electrical performance is formulated as multi-objective optimization problem. Algorithmic frameworks are described for finding Pareto-optimal designs using auxiliary surrogate models and two alternative approaches to design refinement: response correction techniques and co
have tried different techniques for building surrogate models for the antenna data, including interpolation with Radial Basis Functions and approximation with rational functions.
Abstract. A computationally efficient method for design optimization of antennas is discussed. It combines space mapping, used as the optimization engine, and response surface approximation, used to create the fast surrogate model of the optimized antenna.


Get this from a library! Multi-objective design of antennas using surrogate models. [Slawomir Koziel; Adrian Bekasiewicz]
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Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular


This paper tests the efficacy of one sort of surrogate model, the Kriging model, for use in optimising cost-effective, low-energy building designs.
Multi-Objective Design of Antennas Using Variable-Fidelity Simulations and Surrogate Models Article in IEEE Transactions on Antennas and Propagation 61(12):5931-5939 · December 2013 with 29 Reads
Multi-Objective Design of Antennas Using Surrogate Models – Kindle edition by Slawomir Koziel, Adrian Bekasiewicz, Rolf Binder. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Multi-Objective Design of Antennas Using Surrogate Models.
A Surrogate-Based Strategy for Multi-Objective Tolerance Analysis in Electrical Machine Design how global surrogate models (ensembles of fast-to-train artificial neural networks) that are created in order to speed-up the multi-objective evolutionary search can be easily reused to perform a fast tolerance analysis of the optimized designs. Using two industrial optimization scenarios, we
Efficient EM-driven antenna design can be performed using surrogate-based optimisation (SBO). A generic approach to construct surrogate models of antennas involves the use of coarse-discretisation EM simulations (low-fidelity models). A proper selection of the surrogate model fidelity is a key factor that influences both the performance of the design optimisation process and its computational
11 Simulation-Based Multi-objective Antenna Optimization with Surrogate Models. 107 follows. Let cd be a coarse-discretization EM simulation model of the antenna. R Typically, R cd is 10–50 times faster than R f but it is still too expensive for multi- objective optimization. Therefore, we sample the design space using Latin hyper-cube sampling (Beachkofski et al. 2002) and create a fast
Surrogate models can be used to address this lack of data and allow more of the design space to be explored, as well as provide an overall computational saving. In this thesis I have developed two novel long-term prediction methods that investigate the use of ensem-
paper, a multi-objective optimization strategy using Kriging surrogate models, which is determined by comparing the accuracy of three different Kriging models. And the robust multi-objective PSO (MOPSO) is used to get the robust Pareto-optimal values of objective function. In addition, its behavior is investigated through application to the robust version of TEAM problem 22. II. ROBUST

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Multi-Objective Airfoil Shape Optimization Using a

2053. multi-objective optimization design for a battery pack of electric vehicle with surrogate models. cheng lin, fengling gao, wenwei wang, xiaokai chen
Keywords: Antenna design, EM-driven design, surrogate-based optimization, multi-objective optimization 1 Introduction Design and optimization of contemporary antennas is a challenging and multifaceted task.
October 12, 2016 11:58 Multi-Objective Design of Antennas… 9in x 6in b2523-ch06 page 102 102 Multi-Objective Design of Antennas Using Surrogate Models
Read or Download Multi-Objective Design of Antennas Using Surrogate Models PDF. Similar computer systems analysis & design books
The multi-objective optimization problem, variable-fidelity EM-models, design space reduction techniques, as well as methods of constructing the surrogate model are discussed in Section 3. In the same section, we also describe the two alternative algorithmic frameworks for multi-objective antenna design. The multi-objective optimization algorithms are applied to our test case in Section
PDF Download Simulation Driven Aerodynamic Design Using Variable Fidelity Models Books For free written by Leifur Leifsson and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-09 with Technology & …
All the surrogate models in the ensemble are independently coupled to the multi-objective genetic algorithm, and a multiple-realisation optimisation approach is utilised to derive reliable optimal pumping strategies for coastal aquifer management. Reliability of optimal solutions is defined in terms of the percentage of the surrogate models, for which the imposed constraints are satisfied in
“Multi-objective Design Of Antennas Using Surrogate Models” by Slawomir Koziel : People who are searching for Free downloads of books and free pdf copies of these books – “Advances in Multiple Objective and Goal Programming” by Rafael Caballero and Francisco Ruiz, “Interactive Multi-Objective Programming as a Framework for Computer-Aided Control System Design (Lecture Notes in
This work presents a rigorous methodology for expedited simulation-driven multi-objective design of microwave couplers with compact footprints. The proposed approach is a viable alternative for computationally expensive population-based metaheuristics and exploits a surrogate …
Progress In Electromagnetics Research, Vol. 124, 85{100, 2012 HYBRID-SURROGATE-MODEL-BASED EFFICIENT GLOBAL OPTIMIZATION FOR HIGH-DIMENSIONAL ANTENNA DESIGN

Rapid design optimization of antennas using space mapping

Abstract: A computationally-efficient procedure for multi-objective design of antenna structures is presented. Our approach exploits the multi-objective evolutionary algorithm (MOEA) working with a fast antenna surrogate model obtained with kriging interpolation of coarse-discretization simulation data.
In recent years, global optimization using meta-models has been widely applied to design exploration in order to rapidly investigate the design space and find sub-optimal solutions. Indeed, surrogate and reduced-order models can provide a valuable alternative at a much lower computational cost. In this context, this volume offers advanced surrogate modeling applications and optimization
using the realistic CST model of the filtenna (Fig. 1). Figure 3 shows results of the coarse optimization of the surrogate model of the filtenna (wire dipoles in 4NEC):
Surrogate-Based Multi-Objective Engineering Design Robert Carrese The use of surrogate models offers promise toward managing such problems, which are restricted by a computational budget. In this paper, the kriging-assisted user-preference multi-objective particle swarm heuristic is presented, in which less accurate but inexpensive surrogate models are
SpringerBriefs in Optimization showcases algorithmic and theoretical techniques, Multi-objective design of antennas is also covered to some extent. Moreover, we discuss practical issues such as the effect of the coarse- discretization model delity on the nal design quality and the computational cost of the optimization process. Our considerations are illustrated using numerous application
Read “Recent developments in simulation-driven multi-objective design of antennas, Bulletin of the Polish Academy of Sciences Technical Sciences” on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
Previous article in Early View: Principal component analysis for efficient characterization of stochastic electromagnetic fields Previous article in Early View: Principal component analysis for efficient characterization of stochastic electromagnetic fields
Practical antenna design is a multi-objective task. In many situations, it is possible to identify the most important objective (e.g., reflection requirements) and handle antenna design using conventional, single-objective algorithms.
Optimisation and Monitoring Feedback Information Thesis submitted by Sreekanth JANARDHANAN used to train and test different surrogate models using genetic programming. The number of surrogate models in the ensemble is determined by an uncertainty criterion. All the surrogate models in the ensemble are independently coupled to the multi-objective genetic algorithm, and a multiple

Multi-Objective Synthesis of Filtering Dipole Array Based


Constrained multi-objective antenna design optimization

–>This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods.
A methodology for low-cost multi-objective design of antenna structures is proposed. To reduce the computational effort of the design process the initial Pareto front is obtained by optimizing the response surface approximation (RSA) model obtained from low-fidelity EM simulations of the antenna structure of interest. The front is further
By (author): Slawomir Koziel (Reykjavik University, Iceland), Adrian Bekasiewicz (Reykjavik University, Iceland) This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods.
Multi-disciplinary Design Optimization of Supersonic Transport Wing Using Surrogate Model Naoto Seto* *Department of Aerospace Engineering, Tokyo Metropolitan University Key words: Silent Supersonic Transport, Design Exploration, Data Mining, Kriging model Abstract In this study, high efficient design tool is developed with several informatics approaches for multi-disciplinary design
two methodologies for expedited multi-objective design optimization of a six- parameter dielectric resonator antenna (DRA) with respect to three design criteria. The considered solution approaches rely on surrogate-based optimization (SBO)
For multi-objective design of multi-parameter antenna structures, optimization efficiency and computational cost are two major concerns. In this paper, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed to improve global optimization capability by diversity detection operation and mixed population

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Multi-Objective Design of Antennas Using Surrogate Models

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Multi Objective Design of Antennas Using Surrogate Models

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