Study of Relative Entropy Coefficients for Image Segmentation Based on Particle Swarm Optimization

2011 ◽  
Vol 58-60 ◽  
pp. 1056-1060
Author(s):  
You Rui Huang ◽  
Li Guo Qu
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Relative Entropy ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Threshold Method ◽  
Swarm Optimization ◽  
Computation Algorithm

Image segmentation is the basis of image analysis, and because of its simplicity, rapidity and stability, the threshold method is the important one, applying in the image processing and recognition widely. In this paper, a new method is proposed, which based on relative entropy coefficients between random variables. It maximizes the target and background, which is the relative entropy coefficient in probability distribution, and gets the optimal threshold of image segmentation, and then optimizes it using particle swarm algorithm which is an evolutionary computation algorithm. The result of relative entropy coefficients for image segmentation proves its feasibility and better effect.

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

Introduction of New Anti-dumping Algorithm in EU under WTO Law System Based on Particle Swarm Optimization

2021 ◽  
Vol 7 (5) ◽  
pp. 4558-4567
Author(s):  
Wenwen Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Operation Mode ◽  
Particle Swarm ◽  
Algorithm Analysis ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Law System ◽  
Incomplete System ◽  
Swarm Algorithm ◽  
The Eu

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

2019 ◽  
Vol 10 (3) ◽  
pp. 91-106
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Anisotropic Diffusion ◽  
Particle Swarm ◽  
Structural Similarity ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Mr Image ◽  
Swarm Optimization ◽  
Mr Image Segmentation

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

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