A Robust Technique Without Additional Computational Cost in Evolutionary Antenna Optimization

Hu, Caie; Zeng, Sanyou; Jiang, Yuhong; Sun, Jianqing; Sun, Yipeng; Gao, Steven

doi:10.1109/tap.2019.2891661

Cited by 24 publications

(9 citation statements)

References 25 publications

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“…In this paper, the objective function f x ( ) is defined based on the robustness property is taken into account, which is based on our previous work (Hu, C., et al 2019), see Eq (8).…”

Section: Objective and Constraintsmentioning

confidence: 99%

Elliptical Slot Microstrip Patch Antenna Design Based on a Dynamic Constrained Multiobjective Optimization Evolutionary Algorithm

Zeng

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

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Most evolutionary optimization algorithms have already been used for antenna design and shown promising results on improving the performance of the antenna. However, for many real-world antenna optimization problems, they are difficult to solve in that there are highly constrained and multimodal difficulty. These difficulties impede the development of antenna design. In this paper, an elliptical slot microstrip patch antenna design with these difficulties is modeled as a constrained optimization problem (COP). To address the problem, a Dynamic Constrained Multiobjective Optimization Evolutionary Algorithm(DCMOEA) is used. The experimental results show that the optimum antenna with satisfying the design requirement is obtained, and as well as we find the radiation patch should be a whole ellipse instead of subtracting with two ellipses.

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“…In this paper, the objective function f x ( ) is defined based on the robustness property is taken into account, which is based on our previous work (Hu, C., et al 2019), see Eq (8).…”

Section: Objective and Constraintsmentioning

confidence: 99%

Elliptical Slot Microstrip Patch Antenna Design Based on a Dynamic Constrained Multiobjective Optimization Evolutionary Algorithm

Zeng

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

Self Cite

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“…Nevertheless, due to the randomness of the evolutionary algorithm (EA), no matter which kind of algorithm requires a mass of evaluation to obtain satisfying candidate solutions. The evaluation of some practical engineering problems such as antenna design [16], blast optimization [17], trauma system design [18], and power system design [19] are time-consuming and costly so that the expense of obtaining the optimal solution is unaffordable. Therefore, as an efficient tool for expensive optimization problems, the surrogate model [20,21] has attracted much attention from researchers in different fields.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Wang

Xiong

et al. 2021

Complex Intell. Syst.

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Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

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“…Yet, the computational overhead of EM-driven design may prove unacceptable even in the case of local optimization, 12 let alone global search procedures, 13 typically involving population-based metaheuristics. [14][15][16] Incorporation of adjoint sensitivities into gradient-based procedures 17 is one way of mitigating the high cost issue. Other approaches include algorithmic acceleration of conventional routines (eg, sparse sensitivity updates 18,19 ), or surrogate-based optimization (SBO), 20 founded on shifting the computational burden into the fast replacement model (surrogate).…”

Section: Introductionmentioning

confidence: 99%

“…More often than not, the design closure is nowadays performed through numerical optimization, 11 to permit handling of multiple performance figures and simultaneous adjustment all variables. Yet, the computational overhead of EM‐driven design may prove unacceptable even in the case of local optimization, 12 let alone global search procedures, 13 typically involving population‐based metaheuristics 14‐16 …”

Section: Introductionmentioning

confidence: 99%

Cost‐efficient performance‐driven modelling of multi‐band antennas by variable‐fidelity electromagnetic simulations and customized space mapping

Koziel

Pietrenko‐Dabrowska

2020

Int J Numerical Modelling

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Electromagnetic (EM) simulations have become an indispensable tool in the design of contemporary antennas. EM-driven tasks, for example, parametric optimization, entail considerable computational efforts, which may be reduced by employing surrogate models. Yet, data-driven modelling of antenna characteristics is largely hindered by the curse of dimensionality. This may be addressed using the recently reported domain-confinement techniques, especially the nested-kriging framework, which permits rendering of reliable surrogates over wide ranges of antenna parameters while greatly reducing the computational overhead of training data acquisition. Focused on modelling of multi-band antennas, this paper attempts to reduce the cost of surrogate construction even further by incorporating variable-fidelity simulations into the nested kriging. The principal challenge being design-dependent frequency shifts between the models of various fidelities is handled through the development of a customized frequency scaling and output space mapping. Validation is carried out using a dual-band dipole antenna modeled over broad ranges of operating conditions. A small training data set is sufficient to secure the predictive power comparable to that of the nested kriging model set up using solely high-fidelity data, and by far exceeding the accuracy of conventional surrogates. Application examples for antenna optimization and experimental verification of the selected designs are also provided.

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A Robust Technique Without Additional Computational Cost in Evolutionary Antenna Optimization

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 24 publications

References 25 publications

Elliptical Slot Microstrip Patch Antenna Design Based on a Dynamic Constrained Multiobjective Optimization Evolutionary Algorithm

Elliptical Slot Microstrip Patch Antenna Design Based on a Dynamic Constrained Multiobjective Optimization Evolutionary Algorithm

Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Cost‐efficient performance‐driven modelling of multi‐band antennas by variable‐fidelity electromagnetic simulations and customized space mapping

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