A new optimization algorithm applied in electromagnetics — Maxwell’s equations derived optimization (MEDO)

Su, D.; Li, Lilin; Yang, Shunchuan; Li, Bing; Chen, Guangzhi; Xu, Huibin

doi:10.1007/s11432-020-2927-2

Cited by 2 publications

(10 citation statements)

References 46 publications

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“…MEDO is an optimization algorithm, which evolves from the Law of Conservation of Charge and Faraday's Law, and shows strong potential in the applications of electromagnetics [1]. The algorithm is inspired by summarizing the behavior of the charged conductors in time-varying electromagnetic fields.…”

Section: Maxwell's Equations Derived Optimizationmentioning

confidence: 99%

“…They refer to the gravitational acceleration, the magnetic flux density, the per-unit-lengthweight of the conductor, the resistant of the main branch, and the loop inductance of current loop 1, respectively. Reference has proved that the individuals can converge to the optimal location of the objective function under the appropriate parameter settings [1].…”

Section: Maxwell's Equations Derived Optimizationmentioning

confidence: 99%

“…One typical benchmark is calculated by MEDO, with each parameter varies independently. Other parameters take the best values suggested in [1]. The curves of the convergence errors that change with the variation of each parameter are plotted in Fifure 2.…”

Section: Algorithm Descriptionsmentioning

confidence: 99%

“…In electromagnetics and communications, the optimization problems are usually high-dimensional, multimodal and complex, which are challenging for the optimization algorithms. In order to solve the optimization problems in the electromagnetics and communications more efficiently, Maxwell's Equations Derived Optimization (MEDO) is proposed, which is one of the typical representatives of the third type of the algorithms [1].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with other popular optimization algorithms such as Gradient Descent (GD) [2], Particle Swarm Optimization (PSO) [3,4], Differential Evolution (DE) [5], and Adaptive Wind Driven Optimization (AWDO) [6], MEDO can attain more accurate and more stable optimization results for both unimodal functions and multimodal functions [1]. However, the performance of MEDO is sensitive to the parameters, which need to be tuned manually for different problems.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive Maxwell's equations derived optimization and its application in antenna array synthesis

Yang

et al. 2021

China Commun.

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In this paper, a self-adaptive method for the Maxwell's Equations Derived Optimization (MEDO) is proposed. It is implemented by applying the Sequential Model-Based Optimization (SMBO) algorithm to the iterations of the MEDO, and achieves the automatic adjustment of the parameters. The proposed method is named as adaptive Maxwell's equations derived optimization (AMEDO). In order to evaluate the performance of AMEDO, eight benchmarks are used and the results are compared with the original MEDO method. The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters, and at the same time can keep the accuracy and stability. Moreover, the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters. In the end, the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array, and shows great potential in antenna array synthesis.

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Section: Maxwell's Equations Derived Optimizationmentioning

confidence: 99%

Section: Maxwell's Equations Derived Optimizationmentioning

confidence: 99%

Section: Algorithm Descriptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%