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Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Jose E. Rayas-Sanchez, Slawomir Koziel, John W. Bandler
2021 IEEE Journal of Microwaves  
In our fast historical analysis, we emphasize the last two decades of documented microwave design optimization problems and solutions.  ...  subject to statistical uncertainty and varying operating or environmental conditions; 2) the computationally-efficient EM-driven multi-objective design optimization in high-dimensional design spaces including  ...  SIMULATION-DRIVEN SURROGATE-ASSISTED MULTI-OBJECTIVE DESIGN OPTIMIZATION Practical design of high-frequency components, including microwave devices, requires accounting for several performance figures  ... 
doi:10.1109/jmw.2020.3034263 fatcat:a64hobxhfzhe3f2stmhkewvgca

A surrogate-assisted memetic co-evolutionary algorithm for expensive constrained optimization problems

C. K. Goh, D. Lim, L. Ma, Y. S. Ong, P. S. Dutta
2011 2011 IEEE Congress of Evolutionary Computation (CEC)  
This paper presents a surrogate-assisted memetic co-evolutionary framework to tackle both facets of practical problems, i.e. the optimization problems having computationally expensive objectives and constraints  ...  Stochastic optimization of computationally expensive problems is a relatively new field of research in evolutionary computation (EC).  ...  Extensions to enhance search efficiency and approximation accuracy using improved training methods [13] , gradient information [14] and multi-level surrogates [15] [16] were also considered recently  ... 
doi:10.1109/cec.2011.5949693 dblp:conf/cec/GohLMOD11 fatcat:2pguuvwklrcdbepi5x6ci6k2ym

Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems [chapter]

Yew Soon Ong, P. B. Nair, A. J. Keane, K. W. Wong
2005 Studies in Fuzziness and Soft Computing  
In this paper, we present frameworks that employ surrogate models for solving computationally expensive optimization problems on a limited computational budget.  ...  Since EAs typically require thousands of function evaluations to locate a near optimal solution, the use of EAs often becomes computationally prohibitive for this class of problems.  ...  These stochastic methods have been successfully applied to mechanical and aerodynamic problems, including turbine blade design [19] , multi-disciplinary rotor blade design [20] , multi-level aircraft  ... 
doi:10.1007/978-3-540-44511-1_15 fatcat:akme3pq77bc7fjdkra3zzlq7iy


K. Elsayed, C. Lacor
2014 The International Conference on Applied Mechanics and Mechanical Engineering  
This paper deals with an efficient and multi-fidelity design strategy for high dimensional industrial problems.  ...  For global optimization, the Co-Kriging surrogate in conjunction with genetic algorithms (GA) is used. CFD simulations based on the Reynolds stress turbulence model are also used in this study.  ...  These plots can be assist to take the decision of neglecting some variables for high dimensional problems. Tile plot represents an array of contour plots.  ... 
doi:10.21608/amme.2014.35593 fatcat:53rauhge7vasdaeydx3jmqvnky

Rapid Multi-Criterial Antenna Optimization by Means of Pareto Front Triangulation and Interpolative Design Predictors

Slawomir Koziel, Anna Pietrenko-Dabrowska
2021 IEEE Access  
Surrogate-assisted procedures can mitigate the cost issue to a certain extent but construction of reliable metamodels is hindered by the curse of dimensionality, and often highly nonlinear antenna characteristics  ...  INDEX TERMS Antenna optimization, EM-driven design, multi-criterial design, Pareto front triangulation, surrogate modeling. 35670 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  ACKNOWLEDGMENT The authors would like to thank Dassault Systemes, France, for making CST Microwave Studio available.  ... 
doi:10.1109/access.2021.3062449 fatcat:nk5fmuubavggfkqdohstrljr3i

A Batched Scalable Multi-Objective Bayesian Optimization Algorithm [article]

Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, Sam Kwong
2018 arXiv   pre-print
However, most existing surrogate-assisted multi-objective optimization algorithms have three main drawbacks: 1) cannot scale well for solving problems with high dimensional decision space, 2) cannot incorporate  ...  The surrogate-assisted optimization algorithm is a promising approach for solving expensive multi-objective optimization problems.  ...  Surrogate-Assisted Multi-Objective Optimization Surrogate-assisted multi-objective optimization algorithm, such as multi-objective Bayesian optimization (MOBO) [17] , [18] , is promising for solving  ... 
arXiv:1811.01323v1 fatcat:xg2ztstn5ncndgns5zlpkr27fu

Fast Multi-Objective Optimization of Antenna Structures by Means of Data-Driven Surrogates and Dimensionality Reduction

Slawomir Koziel, Anna Pietrenko-Dabrowska
2020 IEEE Access  
SURROGATE-ASSISTED MULTI-OBJECTIVE ANTENNA OPTIMIZATION WITH DIMENSIONALITY REDUCTION This section formulates the proposed multi-objective optimization (MO) methodology.  ...  Constructing the surrogate model only in the region containing the Pareto front allows for a partial alleviation of the dimensionality problem and for improving the computational efficiency of the multi-objective  ... 
doi:10.1109/access.2020.3028911 fatcat:vgtpt6d4pvcytkiyc25caolxta

Global and Local Surrogate-Assisted Differential Evolution for Expensive Constrained Optimization Problems With Inequality Constraints

Yong Wang, Da-Qing Yin, Shengxiang Yang, Guangyong Sun
2018 IEEE Transactions on Cybernetics  
This paper proposes a novel global and local surrogate-assisted differential evolution for solving expensive constrained optimization problems with inequality constraints.  ...  The proposed method consists of two main phases: global surrogate-assisted phase and local surrogate-assisted phase.  ...  Regies [43] developed a RBF-assisted evolutionary programming for high-dimensional constrained expensive black-box optimization problems.  ... 
doi:10.1109/tcyb.2018.2809430 pmid:29993704 fatcat:xoy3pmeyfvaybehtjyy7lwkx4u

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Richard Allmendinger, Michael T. M. Emmerich, Jussi Hakanen, Yaochu Jin, Enrico Rigoni
2017 Journal of Multi-Criteria Decision Analysis  
In this paper we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in non-surrogate-assisted single-objective optimization.  ...  The first question to answer in surrogate-assisted multicriteria optimization is to decide which type of models should be used as the surrogate.  ...  Coello Coello for actively taking part in those discussions and Dr. Karthik Sindhya for his help related to creation of a virtual library for benchmarking.  ... 
doi:10.1002/mcda.1605 fatcat:5ew65lket5azppoo4uw546wdna

Surrogate-assisted evolutionary computation: Recent advances and future challenges

Yaochu Jin
2011 Swarm and Evolutionary Computation  
in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems.  ...  Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single-or multi-objective optimization problems, but also  ...  Multi-level, multi-fidelity heterogeneous surrogates Use of multi-level, multi-fidelity surrogates has already been suggested in [12] .  ... 
doi:10.1016/j.swevo.2011.05.001 fatcat:z3f6vey24fawbmz6gkpcjbnq5q

Performance-Driven Yield Optimization of High-Frequency Structures by Kriging Surrogates

Slawomir Koziel, Anna Pietrenko-Dabrowska
2022 Applied Sciences  
Capitalizing on this idea, this paper discusses a procedure for fast and simple surrogate-based yield optimization of high-frequency structures.  ...  The main concept of the approach is a tailored definition of the surrogate domain, which is based on a couple of pre-optimized designs that reflect the directions featuring maximum variability of the circuit  ...  Acknowledgments: The authors thank Dassault Systemes, France, for making CST Microwave Studio available. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12073697 fatcat:issktvxipvfkll7qtefhds34em

Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

Handing Wang, Yaochu Jin, Chaoli Sun, John Doherty
2018 IEEE Transactions on Evolutionary Computation  
In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions.  ...  Such problems are known as offline data-driven optimization problems.  ...  Since DDEA-SE is ensemble-based, we choose one ensemble-assisted and two single surrogate-assisted data-driven EAs as compared algorithms: committee-based active learning for surrogate-assisted particle  ... 
doi:10.1109/tevc.2018.2834881 fatcat:auy7y4siq5einpetnklvglbeoe

Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey

Hanane Khatouri, Tariq Benamara, Piotr Breitkopf, Jean Demange
2022 Advanced Modeling and Simulation in Engineering Sciences  
AbstractIn design optimization of complex systems, the surrogate model approach relying on progressively enriched Design of Experiments (DOE) avoids efficiency problems encountered when embedding simulation  ...  This paper reviews the strategies that seek to improve surrogate-based optimization efficiency, including ROM, multi-fidelity metamodeling, and DOE enrichment strategies.  ...  CNRS laboratory for their support.  ... 
doi:10.1186/s40323-022-00214-y fatcat:4n7uucw445b5vcalrrxcrcwctq

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, C. Miao
2015 Hydrology and Earth System Sciences  
LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ~10<sup>5</sup>–10<sup>6</sup>).  ...  in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional  ...  It is suitable for high-dimensional, small-sample nonlinear regression problems.  ... 
doi:10.5194/hess-19-2409-2015 fatcat:gietgda7wzcpvlfnkrbcd3ojau

To the special Issue on "Metaheuristics for optimization of complex process engineering"

Jinliang Ding, Yaochu Jin
2016 Natural Computing  
The authors describe a self-adaptive fruit fly optimization algorithm for solving high-dimensional global optimization problems.  ...  The problem is a time-varying, nonlinear and highly constrained multi-objective optimization problem. An ensemble of selection method is adopted in the algorithm to enhance the performance.  ... 
doi:10.1007/s11047-016-9578-x fatcat:rspylnm6jne7jlkho5okzx5r5e
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