Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

Purpose This paper aims to assess control parameter setup and its effect on computational cost and performance of deterministic procedures for multi-objective design optimization of expensive simulation models of antenna structures. Design/methodology/approach A deterministic algorithm for cost-efficient multi-objective optimization of antenna structures has been assessed. The algorithm constructs a patch connecting extreme Pareto-optimal designs (obtained by means of separate single-objective optimization runs). Its performance (both cost- and quality-wise) depends on the dimensions of the so-called patch, an elementary region being relocated in the course of the optimization process. The cost/performance trade-offs are studied using two examples of ultra-wideband antenna structures and the optimization results are compared to draw conclusions concerning the algorithm robustness and determine the most advantageous control parameter setups. Findings The obtained results indicate that the investigated algorithm is very robust, i.e. its performance is weakly dependent on the control parameters setup. At the same time, it is found that the most suitable setups are those that ensure low computational cost, specifically non-uniform ones generated on the basis of sensitivity analysis. Research limitations/implications The study provides recommendations for control parameter setup of deterministic multi-objective optimization procedure for computationally efficient design of antenna structures. This is the first study of this kind for this particular design procedure, which confirms its robustness and determines the most suitable arrangement of the control parameters. Consequently, the presented results permit full automation of the surrogate-assisted multi-objective antenna optimization process while ensuring its lowest possible computational cost. Originality/value The work is the first comprehensive validation of the sequential domain patching algorithm under various scenarios of its control parameter setup. The considered design procedure along with the recommended parameter arrangement is a robust and computationally efficient tool for fully automated multi-objective optimization of expensive simulation models of contemporary antenna structures.

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Variable-fidelity CFD models and co-Kriging for expedited multi-objective aerodynamic design optimization

Engineering Computations ◽

10.1108/ec-09-2015-0277 ◽

2016 ◽

Vol 33 (8) ◽

pp. 2320-2338 ◽

Cited By ~ 5

Author(s):

Slawomir Koziel ◽

Yonatan Tesfahunegn ◽

Leifur Leifsson

Keyword(s):

Search Space ◽

Aerodynamic Design ◽

Multi Objective Optimization ◽

Content Type ◽

Multi Objective ◽

Cfd Models ◽

Modeling Techniques ◽

Algorithmic Framework ◽

Variable Fidelity ◽

Aerodynamic Surfaces

Purpose Strategies for accelerated multi-objective optimization of aerodynamic surfaces are investigated, including the possibility of exploiting surrogate modeling techniques for computational fluid dynamic (CFD)-driven design speedup of such surfaces. The purpose of this paper is to reduce the overall optimization time. Design/methodology/approach An algorithmic framework is described that is composed of: a search space reduction, fast surrogate models constructed using variable-fidelity CFD models and co-Kriging, and Pareto front refinement. Numerical case studies are provided demonstrating the feasibility of solving real-world problems involving multi-objective optimization of transonic airfoil shapes and accurate CFD simulation models of such surfaces. Findings It is possible, through appropriate combination of surrogate modeling techniques and variable-fidelity models, to identify a set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity CFD simulations of the respective surfaces. Originality/value The proposed aerodynamic design optimization algorithmic framework is novel and holistic. It proved useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search space, which is extremely challenging when using conventional methods due to the excessive computational cost.

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Multi-objective Optimization Using Variable-Fidelity Models and Response Correction

Simulation-Driven Design by Knowledge-Based Response Correction Techniques ◽

10.1007/978-3-319-30115-0_11 ◽

2016 ◽

pp. 193-210

Author(s):

Slawomir Koziel ◽

Leifur Leifsson

Keyword(s):

Multi Objective Optimization ◽

Multi Objective ◽

Response Correction ◽

Variable Fidelity

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Multi-Objective Optimization of Complex Multibody Systems by Coupling HOTINT With MagOpt

Volume 6: 12th International Conference on Multibody Systems, Nonlinear Dynamics, and Control ◽

10.1115/detc2016-60204 ◽

2016 ◽

Cited By ~ 1

Author(s):

Alexander Humer ◽

Gerald Jungmayr ◽

Werner Koppelstätter ◽

Markus Schörgenhumer ◽

Siegfried Silber ◽

...

Keyword(s):

Simulation Models ◽

Simulation Software ◽

Pareto Optimum ◽

Multi Objective Optimization ◽

Mechatronic Systems ◽

Multi Objective ◽

Detailed Simulation ◽

Physics Model ◽

The Individual ◽

Fully Coupled

Multi-objective optimization of complex mechatronic systems does not only require detailed simulation models for the individual components, but often demands a multi-physics model that adequately describes their fully coupled behavior. Typically, such a multi-physics model cannot be realized within a single simulation software but rather necessitates the combination of diverse tools. The efficient handling of dependencies among several components in various physical domains within a heterogeneous simulation environment is a key challenge in the context of optimization. The present paper illustrates the multi-objective optimization of a magnetically levitated rotor combining the open-source multibody simulation software HOTINT and the optimization tool MagOpt. MagOpt uses evolutionary algorithms to determine the Pareto-optimum of the multi-physics model, which comprises the flexible multibody system, electromagnetic components and the control system.

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Improving Multi-Objective Genetic Algorithm Efficiency for Computational Expensive Problems Adopting Online Variable-Fidelity Metamodel

Volume 2B: 44th Design Automation Conference ◽

10.1115/detc2018-86308 ◽

2018 ◽

Author(s):

Leshi Shu ◽

Ping Jiang ◽

Qi Zhou ◽

Xiangzheng Meng ◽

Yahui Zhang

Keyword(s):

Optimization Problems ◽

Computational Cost ◽

Simulation Models ◽

Pareto Solutions ◽

Multi Objective ◽

Algorithm Efficiency ◽

Multi Objective Genetic Algorithm ◽

Computation Process ◽

Practical Engineering ◽

Variable Fidelity

Multi-objective genetic algorithms (MOGAs) are effective ways for obtaining Pareto solutions of multi-objective optimization problems. However, the high computational cost of MOGAs limits their applications to practical engineering optimization problems involving computational expensive simulations. To address this issue, a variable-fidelity metamodel (VFM) assisted MOGA approach is proposed, in which VFM is embedded in the computation process of MOGA to replace expensive simulation models. The VFM is updated in the optimization process considering the cost of simulation models with different fidelity and the effects of the VFM uncertainty. A numerical example and an engineering case are used to demonstrate the applicability and efficiency of the proposed approach. The results show that the proposed approach can obtain Pareto solutions with high quality and it outperforms the other three existing approaches in terms of computational efficiency.

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Multi-objective optimization for sustainable groundwater management by developing of coupled quantity-quality simulation-optimization model

Journal of Hydroinformatics ◽

10.2166/hydro.2017.007 ◽

2017 ◽

Vol 19 (6) ◽

pp. 973-992 ◽

Cited By ~ 16

Author(s):

Asghar Kamali ◽

Mohammad Hossein Niksokhan

Keyword(s):

Water Quality ◽

Optimization Model ◽

Simulation Optimization ◽

Simulation Models ◽

Multi Objective Optimization ◽

Management Scenarios ◽

Sustainability Index ◽

Multi Objective ◽

Previous Operation ◽

The Stability

Abstract This study addresses the issue of optimal management of aquifers using a mathematical simulation- optimization model which relies on the stability of water quality and quantity, considering salinity. In this research first we developed a hydrological model (SWAT) to estimate recharge rates and its spatiotemporal distribution. Then, groundwater simulation of the basin was simulated and calibrated using MODFLOW 2000 and water quality was simulated and calibrated using MT3DMS. Afterwards, a multi-objective optimization model (MOPSO) and embed simulation models as tools to assess the objective function was carried out in order to produce a simulation-optimization model. Finally, a sustainability index to assess Pareto front's answers and three management scenarios (continuing previous operation, 30% increasing and reduction in previous operation) was developed. The results show that the majority of Pareto optimal answers have more sustainability index than a 30% reduction of operation with the best answer of 0.059. Relatively, the sustainability index of 30% reduction of operation is 0.05.

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On deterministic procedures for low-cost multi-objective design optimization of miniaturized impedance matching transformers

Engineering Computations ◽

10.1108/ec-01-2016-0046 ◽

2017 ◽

Vol 34 (2) ◽

pp. 403-419 ◽

Cited By ~ 3

Author(s):

Slawomir Koziel ◽

Adrian Bekasiewicz

Keyword(s):

Design Optimization ◽

Pareto Front ◽

Low Cost ◽

Impedance Matching ◽

Simulation Models ◽

Multi Objective Optimization ◽

Content Type ◽

Multi Objective ◽

Modeling Techniques ◽

Em Simulation

Purpose This paper aims to investigate deterministic strategies for low-cost multi-objective design optimization of compact microwave structures, specifically, impedance matching transformers. The considered methods involve surrogate modeling techniques and variable-fidelity electromagnetic (EM) simulations. In contrary to majority of conventional approaches, they do not rely on population-based metaheuristics, which permit lowering the design cost and improve reliability. Design/methodology/approach There are two algorithmic frameworks presented, both fully deterministic. The first algorithm involves creating a path covering the Pareto front and arranged as a sequence of patches relocated in the course of optimization. Response correction techniques are used to find the Pareto front representation at the high-fidelity EM simulation level. The second algorithm exploits Pareto front exploration where subsequent Pareto-optimal designs are obtained by moving along the front by means of solving appropriately defined local constrained optimization problems. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving expensive EM-simulation models of impedance transformer structures. Findings It is possible, by means of combining surrogate modeling techniques and constrained local optimization, to identify the set of alternative designs representing Pareto-optimal solutions, in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Multi-objective optimization for the considered class of structures can be realized using deterministic approaches without defaulting to evolutionary methods. Research limitations/implications The present study can be considered a step toward further studies on expedited optimization of computationally expensive simulation models for miniaturized microwave components. Originality/value The proposed algorithmic solutions proved useful for expedited multi-objective design optimization of miniaturized microwave structures. The problem is extremely challenging when using conventional methods, in particular evolutionary algorithms. To the authors’ knowledge, this is one of the first attempts to investigate deterministic surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

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