Particle swarm optimisation for Pareto optimal solutions in electromagnetic shape design

Baumgartner, Ulrike; Magele, Christian; Preis, Kurt; Renhart, Werner

doi:10.1049/ip-smt:20040631

Cited by 15 publications

(3 citation statements)

References 2 publications

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“…The closer an individual is to the Pareto-optimal front, the higher the probability is to participate in the creation of the next generation. A Paretooptimal solution is defined as the best achievable value for one objective without deteriorating at least one of the other objectives [41]. The crowding distance criterion [42] is implemented in order to maintain the diversity in each generation and to avoid the convergence to very similar solutions.…”

Section: Design Techniquementioning

confidence: 99%

Fully Metallic Dual-Band 3-D Wire Antenna for Wi-Fi and Wi-Max Applications

Benmahmoud¹,

Lemaître-Auger²,

Tedjini³

2021

PIER C

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A segmented three-dimensional wire monopole antenna is proposed and optimized to operate in both the Wi-Fi and Wi-Max frequency bands (2.4-2.48 and 3.3-3.7 GHz). The fabrication of the antenna employs both three dimension printing and foundry techniques. The design occupies a total volume of 33.8 mm × 30.4 mm × 37.4 mm, which is equivalent to 0.28λ 0 × 0.25λ 0 × 0.30λ 0 , where λ 0 is the central wavelength of the lower band. The measurements agree with the simulations and show that the antenna has a −10 dB impedance bandwidth of 7.53% (2.36 to 2.55 GHz) and 53.87% (2.78 to 4.43 GHz) and a measured −3 dB axial ratio bandwidth of 19.06% (3.18 to 3.85 GHz) for the second band. For the first band, simulations indicate that the polarization is elliptical. The radiation pattern is a near hemispherical coverage toward the upper hemisphere. The measured maximum gain values are 5.6 and 7.3 dB for the lower and upper bands, respectively. The simulated radiation efficiency is higher than 98%.

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Section: Design Techniquementioning

confidence: 99%

Fully Metallic Dual-Band 3-D Wire Antenna for Wi-Fi and Wi-Max Applications

Benmahmoud¹,

Lemaître-Auger²,

Tedjini³

2021

PIER C

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“…There are several algorithms to find the non‐inferior points in a multiobjective optimisation problem. Such as the GA [16], differential evolution [17], particle swarm optimisation [18], and simulated annealing [19] received more attention.…”

Section: Multiobjective Optimisationmentioning

confidence: 99%

Design of a controller for rail eddy current brake system

Zamani¹

2014

IET Electrical Systems in Transportation

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A rail eddy current brake (ECB) controller is proposed to produce the optimum braking and attractive forces using the variation of the reference current according to the speed. To improve the operation of the ECB, an optimal design of the controller is carried out by means of finite element method. Kriging method is utilised to reduce the computational costs. Additionally, the genetic algorithm is used as a multiobjective optimisation method to find the optimum current references in different speeds.

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“…Lately, the real-life case studies of a neural network with stochastic search learning algorithm are developed [29]- [33]. PSO can solve multiobjective problems by using Pareto theory [34], [35] and penalty weight [36].…”

mentioning

confidence: 99%

Improved Hybrid Particle Swarm Optimized Wavelet Neural Network for Modeling the Development of Fluid Dispensing for Electronic Packaging

Ling

Leung

et al. 2008

IEEE Trans. Ind. Electron.

157

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An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for Modeling the development of Fluid Dispensing for Electronic Packaging (MFD-EP) is presented in this paper. In modeling the fluid dispensing process, it is important to understand the process behavior as well as determine the optimum operating conditions of the process for a high-yield, low-cost, and robust operation. Modeling the fluid dispensing process is a complex nonlinear problem. This kind of problem is suitable to be solved by applying a neural network. Among the different kinds of neural networks, the WNN is a good choice to solve the problem. In the proposed WNN, the translation parameters are variables depending on the network inputs. Due to the variable translation parameters, the network becomes an adaptive one that provides better performance and increased learning ability than conventional WNNs. An improved hybrid PSO is applied to train the parameters of the proposed WNN. The proposed hybrid PSO incorporates a wavelet-theory-based mutation operation. It applies the wavelet theory to enhance the PSO in more effectively exploring the solution space to reach a better solution. A case study of MFD-EP is employed to demonstrate the effectiveness of the proposed method.Index Terms-Modeling, particle swarm optimization (PSO), wavelet neural network (WNN), wavelet theory.

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Particle swarm optimisation for Pareto optimal solutions in electromagnetic shape design

Cited by 15 publications

References 2 publications

Fully Metallic Dual-Band 3-D Wire Antenna for Wi-Fi and Wi-Max Applications

Fully Metallic Dual-Band 3-D Wire Antenna for Wi-Fi and Wi-Max Applications

Design of a controller for rail eddy current brake system

Improved Hybrid Particle Swarm Optimized Wavelet Neural Network for Modeling the Development of Fluid Dispensing for Electronic Packaging

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