A new optimizer using particle swarm theory

Eberhart, Russell C.; Kennedy, James

doi:10.1109/mhs.1995.494215

Cited by 11,251 publications

(6,025 citation statements)

References 5 publications

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“…PSO is a population based evolutionary optimization algorithm which consists of a number of particles that are potential solutions to the optimization problem [25]. Each particle has a position and a velocity which are initialized with random values.…”

Section: Snake-particle Swarm Optimisationmentioning

confidence: 99%

An efficient intelligent analysis system for confocal corneal endothelium images

Sharif

Qahwaji

Shahamatnia

et al. 2015

Computer Methods and Programs in Biomedicine

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A confocal microscope provides a sequence of images of the corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient's cornea. Hybrid model based on snake and particle swarm optimisation (S-PSO) is proposed in this paper to analyse the confocal endothelium images. The proposed system is able to pre-process (quality enhancement, noise reduction), detect the cells, measure the cell density and identify abnormalities in the analysed data sets. Three normal corneal data sets acquired using confocal microscope, and two abnormal endothelium images associated with diseases have been investigated in the proposed system. Promising results are achieved and the performance of this system are compared with the performance of two morphological based approaches. The developed system can be deployed as clinical tool to underpin the expertise of ophthalmologists in analysing confocal corneal images.

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Section: Snake-particle Swarm Optimisationmentioning

confidence: 99%

An efficient intelligent analysis system for confocal corneal endothelium images

Sharif

Qahwaji

Shahamatnia

et al. 2015

Computer Methods and Programs in Biomedicine

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“…PSO inventors [18,19] were inspired by such scenarios based on natural processes, explained below, to solve the optimization problems. Suppose the following scenario: a group of birds are randomly searching for food in an area, where there is only one piece of food available and none of them knows where it is, but they can estimate how far it would be.…”

Section: Particle Swarm Optimization Algorithms (Pso)mentioning

confidence: 99%

A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA

Aydın¹,

Kwan²,

Leung³

et al. 2013

Applied Soft Computing

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Abstract. Multiuser scheduling is an important aspect in the performance optimization of a wireless network since it allows multiple users to access a shared channel efficiently by exploiting multiuser diversity. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. In this paper, a more realistic imperfect CSI feedback, in the form of a finite set of Channel Quality Indicator (CQI) values, is assumed as specified in the HSDPA standard. A mathematical model of the problem is developed for use in the optimization process. A hybrid heuristic approach based on particle swarm optimization and simulated annealing is used to solve the problem. Simulation results indicate that the hybrid approach outperforms individual implementations of both simulated annealing and particle swarm optimisation.

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“…Some global optimal searching method such as genetic algorithm [22][23][24], neural network [25], have be proposed to search the global extreme of the nonlinear functional problem. In the 1995, the Kennnedy and Eberhart first proposed the particle swarm optimization (PSO) [30]. The particle swarm optimization is a population based stochastic optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Time Domain Inverse Scattering of a Two-Dimensional Homogenous Dielectric Object With Arbitrary Shape by Particle Swarm Optimization

Huang¹,

Chiu²,

Li³

et al. 2008

PIER

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Abstract-This paper presents a computational approach to the twodimensional time domain inverse scattering problem of a dielectric cylinder based on the finite difference time domain (FDTD) method and the particle swarm optimization (PSO) to determine the shape, location and permittivity of a dielectric cylinder. A pulse is incident upon a homogeneous dielectric cylinder with unknown shape and dielectric constant in free space and the scattered field is recorded outside. By using the scattered field, the shape and permittivity of the dielectric cylinder are reconstructed. The subgridding technique is implemented in the FDTD code for modeling the shape of the cylinder more closely. In order to describe an unknown cylinder with arbitrary shape more effectively, the shape function is expanded by closed cubicspline function instead of frequently used trigonometric series. The inverse problem is resolved by an optimization approach, and the global searching scheme PSO is then employed to search the parameter space. Numerical results demonstrate that, even when the initial guess is far away from the exact one, good reconstruction can be obtained. In addition, the effects of Gaussian noise on the reconstruction results are investigated. Numerical results show that even the measured scattered E fields are contaminated with some Gaussian noise, PSO can still yield good reconstructed quality.

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A new optimizer using particle swarm theory

Cited by 11,251 publications

References 5 publications

An efficient intelligent analysis system for confocal corneal endothelium images

An efficient intelligent analysis system for confocal corneal endothelium images

A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA

Time Domain Inverse Scattering of a Two-Dimensional Homogenous Dielectric Object With Arbitrary Shape by Particle Swarm Optimization

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