Particle Swarm Optimization: A Study of Variants and Their Applications

Kumar, Ashok; Singh, Brajesh Kumar; Patro, B. D. K.

doi:10.5120/ijca2016908406

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…PSO has been successfully applied either independently or combined or hybridized with algorithms to solve problems in numerous fields of application ranging from industrial, medical, social, computational, and many more, such as fault-tolerant scheduling of real-time tasks on multiprocessor based systems [24], image registration [25,26], optimization of a five-echelon supply chain network [22], acute leukemia segmentation [23] and harmonic reduction in multilevel cascade inverters for renewable energy sources [27]. In a quest for improved performance of optimization for solving problems of specific type or in specific domains of application, many variants of the basic PSO has been proposed by researchers from different fields of study [28,29]. In this paper, we integrate PSO, specifically the WIPSO with the DWT to achieve the core goal of effective image registration.…”

Section: Discrete Wavelength Transformmentioning

confidence: 99%

A Low Overhead Image Registration Algorithm using DWT and WIPSO for Resource Constrained SBC based Embedded System Application

Das¹,

Pramanik²,

Samal³

et al. 2019

IJRTE

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Image registration is a vital but integral component of any image processing application. Fundamentally, image registration involves transforming two or more sets of image data derived from different sources, collected at different instant of times, taken from different perspectives and from different viewpoints, all from different frames of reference into an image in a single coordinate system, a single frame of reference. Over the years, there have been many image registration techniques developed and used in variety of application fields. This paper proposes a computationally low overhead image registration technique that has been tested with variety of images using weight improved particle swarm optimization (WIPSO) algorithm integrated with discrete wavelet transform (DWT). Fundamentally, the proposed scheme comprises of a two-step approach, where the first step involves extraction of the random image from the source image which will serve as an ingredient for the formation of the particle swarm to be used in the WIPSO and the second step involves an explorative search for the target image in the area of interest from the selected population using WIPSO technique. Extensive simulation has established the effectiveness of the proposed soft-computing technique for variety of image registration application; even suitable for deployment in many resource-constrained single board computer based embedded system applications.

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Section: Discrete Wavelength Transformmentioning

confidence: 99%

A Low Overhead Image Registration Algorithm using DWT and WIPSO for Resource Constrained SBC based Embedded System Application

Das¹,

Pramanik²,

Samal³

et al. 2019

IJRTE

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“…La velocidad de la partícula se modifica en cada iteración de la ecuación (1) utilizando la velocidad anterior multiplicada por una constante , un componente cognitivo y un componente social. El componente cognitivo representa la distancia entre la posición actual y la mejor posición conocida por esa partícula ( ) y el componente social representa la distancia entre la posición actual y la mejor posición del cúmulo (g ) [8].…”

Section: Algoritmo Optimización Por Cúmulo De Partículas (Pso)unclassified

C-PSO: Optimización por cúmulo de partículas incrustando la ley de Coulomb

Monje¹,

Acosta²,

García³

et al. 2018

RCS

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“…Step 4: Stop the algorithm if it reaches a specified stopping point, otherwise go to step 2 [26,27].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

An Artificial Predictive Modeling Framework for Automatically Detecting Problematic Use of Internet

Hassani¹,

Pakzad²,

Asghari³

2018

IJCA

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The Internet is one of the most influential new communications technologies that have been affected both social and health aspects of users' lives. Despite many advantages and its positive pointes in communication, the excessive use of Internet make a serious danger that has adverse impacts on overall health. The inability of individuals to control the Internet use has been defined as Internet Addiction (IA). There are a lot of studies on IA over the topics of diagnosis, phenomenology, epidemiology, and treatment based on statistical analysis, but a few research works are conducted by machine learning techniques. So our research focuses on diagnosing and counseling to prevent IA by developing prediction models based on ensemble method with machine learning algorithms and finding the most important risk factors in diagnosing the status of users' IA. Experiment results demonstrate that this hybrid machine learning model by considering students field and location along with items 1, 3, 4, 5,10,12,14,15,19,20 from Internet Addiction Test (IAT) as the most important risk factors can identify the status of users' IA with 99.75% accuracy attains a performance superior to traditional single classifier systems.

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Particle Swarm Optimization: A Study of Variants and Their Applications

Cited by 12 publications

References 40 publications

A Low Overhead Image Registration Algorithm using DWT and WIPSO for Resource Constrained SBC based Embedded System Application

A Low Overhead Image Registration Algorithm using DWT and WIPSO for Resource Constrained SBC based Embedded System Application

C-PSO: Optimización por cúmulo de partículas incrustando la ley de Coulomb

An Artificial Predictive Modeling Framework for Automatically Detecting Problematic Use of Internet

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