Hybrid quantum particle swarm optimisation to calculate wideband Green's functions for microstrip structures

Hedayati, Maziar; Firouzeh, Zaker Hossein; Nekoei, Hamed Khayam

doi:10.1049/iet-map.2015.0169

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Inspired by quantum mechanics [15] and the trajectory analysis of particle swarm optimization [16,17], in order to enhance the global searching ability, we combine the quantum-inspired evolutionary algorithm (QEA) and particle swarm optimization (PSO), and propose a new quantum-behaved particle swarm optimization (QPSO). In QPSO, to enhance the global searching ability, the mean individual best-known position of the population, denoted as mbest, is introduced, such that particle x i can be updated according to the following equations:…”

Section: Methodsmentioning

confidence: 99%

Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

Huo

Ge-fu

et al. 2018

Sustainability

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This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will be solved by the proposed quantum-particle swarm optimization algorithm. The three-echelon supply chain, consisting of suppliers, distribution centers, and retailers, is established based on the number and location of suppliers, the transport method from suppliers to distribution centers, and the quantity of products to be transported from suppliers to distribution centers and from these centers to retailers. Then, a quantum-particle swarm optimization is described as its performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to balance the economic benefit and environmental effect.

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Section: Methodsmentioning

confidence: 99%

Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

Huo

Ge-fu

et al. 2018

Sustainability

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“…CI technique has been first introduced by Fang [14] and then improved by Chow [15]. Over the years, this technique has been widely used by different research groups who applied it to many different structures and improved its properties [16][17][18][19][20][21][22][23][24]. Accuracy, simplicity, rapid convergence, low computational cost, and short computational time are some of the advantages of this technique.…”

Section: Introductionmentioning

confidence: 99%

Novel two‐dimensional periodic Green's function for the efficient analysis of structures made of elements perpendicular to the dielectric substrate by complex images

Valizdeh

Ameri

Faraji‐Dana

2019

IET Microwaves, Antennas & Propagation

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Here, a simple, accurate and rapidly convergent method based on complex images (CI) technique is developed to derive a closed‐form Green's function for periodic structures perpendicular to a substrate. The Green's function is represented in a two criteria form, based on the distance of the field point from the source point. That is, for the small field point to source point distances, it is given in terms of a CI representation, while for the larger field point to source point distances, the contribution of the poles in the spectral domain function is used through the application of the residue theorem. The main advantage of the derived Green's function lies on its accuracy, ease of computation, and fast convergence. The usage of the derived closed‐form Green's functions in the mixed potential integral equation (MPIE), an efficient method for analysing periodic structures, has been introduced. The proposed method is applied to several examples, and the results are in good agreement with the simulations in commercial software.

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Optimal Artificial Neural Network using Particle Swarm Optimization

Alam,

Sallem,

Pereira

et al. 2023

E3S Web of Conf.

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Artificial neuron networks (ANNs) are widely used for data analyticS in broad areas of engineering applications and commercial services. The ANN has one to two hidden layers. In advanced ANN, multiple-layer ANN is used where the network extracts different features until it can recognize what it is looking for through deep learning approaches. Usually, a backpropagation algorithm is used to train the network and fix weights and biases associated with each network neuron. This paper proposes a particle swarm optimization (PSO) based algorithm for training ANN for better performance and accuracy. Two types of ANN models and their training using PSO have been developed. The performance of the developed models has been analyzed on a standard dataset. Also, the effectiveness and suitability of the developed approach have been demonstrated through statistics of the obtained results.

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Hybrid quantum particle swarm optimisation to calculate wideband Green's functions for microstrip structures

Cited by 3 publications

References 25 publications

Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

Novel two‐dimensional periodic Green's function for the efficient analysis of structures made of elements perpendicular to the dielectric substrate by complex images

Optimal Artificial Neural Network using Particle Swarm Optimization

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