Comparison of PSO and ABC: From A Viewpoint of Learning

Hao, Guosheng; Wang, Gai-Ge; Zhang, Zhaojun; Zou, Dexuan

doi:10.12783/dtcse/aita2017/15999

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In case of ABC, the whole swarm is subdivided into three groups of bees: scout, employed, and onlooker bees. The scouts randomly generate solutions (exploration), while the onlooker and employed are responsible for their selection and updating (exploitation), respectively [31]. The employed bees produce new solutions based on the information from the scout and onlooker bees.…”

Section: Abc Optimization Based Methodsmentioning

confidence: 99%

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

Jarndal

Crupi

Raffo

et al. 2021

IEEE J. Electron Devices Soc.

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In this paper, a new modeling technique is proposed for extracting small-signal lumped-element equivalent-circuit models for microwave transistors. The proposed procedure is based on using an optimization approach that is improved by targeting a quasi-static behavior as additional objective function rather than only minimizing the error between the simulated and measured scattering parameters. The validity of the developed modeling methodology is successfully demonstrated by considering a 0.25x1000 m 2 gallium nitride (GaN) highelectron-mobility transistor (HEMT) as a case study.

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Section: Abc Optimization Based Methodsmentioning

confidence: 99%

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

Jarndal

Crupi

Raffo

et al. 2021

IEEE J. Electron Devices Soc.

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“…These search characteristics allow discoveries at different levels by using different selection and search operators and thus have high performances for the optimization [13,15]. Therefore, we examined the capabilities of iterative thresholding of the PSO and ABC algorithms since they are two heuristic algorithms frequently compared in the literature and illustrated to be used for the detection of bruised apples in a hybrid way with the CNN model [13,16].…”

Section: Introductionmentioning

confidence: 99%

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Hekim¹,

Cömert²,

Adem³

2020

Turk J Elec Eng & Comp Sci

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In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stemcalyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken from 200 apples, of which 100 were bruised and 100 healthy. In order to increase the success of detection and classification, adaptive histogram equalization (AHE), edge detection, and morphological operations were applied to the images in the preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of the images, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined by means of the ABC/PSO-based iterative thresholding approach on the images whose stem-calyx regions were discarded, and then the bruised areas on the images with no stem-calyx anatomical regions were detected by using the determined threshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. In order to illustrate the classification success of our approaches, the same classification experiments were reimplemented by directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimental results showed that the hybrid model proposed in this paper was more successful than the CNN model in which ABCand PSO-based iterative threshold approaches were not used.

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Comparison of Two Swarm Intelligence Algorithms: From the Viewpoint of Learning

Hao

Wan

et al. 2018

Intelligent Computing Methodologies

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Comparison of PSO and ABC: From A Viewpoint of Learning

Cited by 4 publications

References 14 publications

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

An Improved Transistor Modeling Methodology Exploiting the Quasi-Static Approximation

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Comparison of Two Swarm Intelligence Algorithms: From the Viewpoint of Learning

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