A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C-Means Algorithm (NIS-IFCM)

2019 ◽  
Vol 34 (05) ◽  
pp. 2055011
Author(s):  
Jing Zhao ◽  
Xiaoli Wang ◽  
Ming Li
Keyword(s):  
Computer Vision ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Classical Problem ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
Novel Method ◽  
Fuzzy C Means Algorithm ◽  
Novel Algorithm

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

scholarly journals A Velocity-Combined Local Best Particle Swarm Optimization Algorithm for Nonlinear Equations

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhigang Lian ◽  
Songhua Wang ◽  
Yangquan Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Equations ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
The Given ◽  
Novel Algorithm

Many people use traditional methods such as quasi-Newton method and Gauss–Newton-based BFGS to solve nonlinear equations. In this paper, we present an improved particle swarm optimization algorithm to solve nonlinear equations. The novel algorithm introduces the historical and local optimum information of particles to update a particle’s velocity. Five sets of typical nonlinear equations are employed to test the quality and reliability of the novel algorithm search comparing with the PSO algorithm. Numerical results show that the proposed method is effective for the given test problems. The new algorithm can be used as a new tool to solve nonlinear equations, continuous function optimization, etc., and the combinatorial optimization problem. The global convergence of the given method is established.

Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization

2015 ◽  
Vol 23 (05) ◽  
pp. 667-683 ◽  
Author(s):  
Troudi Ahmed ◽  
Bouzbida Mohamed ◽  
Chaari Abdelkader
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Kernel Method ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi–Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global optimization technique. Thus, the incorporation of local research capacity of the KNEPCM algorithm and the global optimization ability of the PSO algorithm can solve these problems. In this paper, a new clustering algorithm called KNEPCM-PSO is proposed. This algorithm is a combination between Kernel New Extended Possibilistic C-Means algorithm (KNEPCM) and Particle Swarm Optimization (PSO). The effectiveness of this algorithm is tested on nonlinear systems and on an electro-hydraulic system.

Modified and Optimized Method for Segmenting Pulmonary Parenchyma in CT Lung Images, Based on Fractional Calculus and Natural Selection

2017 ◽  
Vol 28 (5) ◽  
pp. 721-732 ◽  
Author(s):  
S. Pramod Kumar ◽  
Mrityunjaya V. Latte
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Disease Diagnosis ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Otsu Method ◽  
Speed Up ◽  
Pulmonary Parenchyma ◽  
Aided Diagnosis

Abstract Computer-aided diagnosis of lung segmentation is the fundamental requirement to diagnose lung diseases. In this paper, a two-dimensional (2D) Otsu algorithm by Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) is proposed to segment the pulmonary parenchyma from the lung image obtained through computed tomography (CT) scans. The proposed method extracts pulmonary parenchyma from multi-sliced CT. This is a preprocessing step to identify pulmonary diseases such as emphysema, tumor, and lung cancer. Image segmentation plays a significant role in automated pulmonary disease diagnosis. In traditional 2D Otsu, exhaustive search plays an important role in image segmentation. However, the main disadvantage of the 2D Otsu method is its complex computation and processing time. In this paper, the 2D Otsu method optimized by DPSO and FODPSO is developed to reduce complex computations and time. The efficient segmentation is very important in object classification and detection. The particle swarm optimization (PSO) method is widely used to speed up the computation and maintain the same efficiency. In the proposed algorithm, the limitation of PSO of getting trapped in local optimum solutions is overcome. The segmentation technique is assessed and equated with the traditional 2D Otsu method. The test results demonstrate that the proposed strategy gives better results. The algorithm is tested on the Lung Image Database Consortium image collections.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Multi-Threshold Image Segmentation Based on Improved Particle Swarm Optimization and Maximum Entropy Method

2014 ◽  
Vol 989-994 ◽  
pp. 3649-3653 ◽  
Author(s):  
Ya Ti Lu ◽  
Wen Li Zhao ◽  
Xiao Bo Mao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Particle Swarm ◽  
Entropy Method ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Threshold Segmentation ◽  
Expansion Model

Both maximum entropy method and Particle swarm optimization (PSO) are common threshold segmentation methods which have been used not only in image segmentation, but also in multi-threshold segmentation. Maximum entropy method is time-consuming, PSO may easily get trapped in a local optimum. In view of this concerning issue, we propose the PSO and maximum entropy are combined to make improvements on the PSO introduced in expansion model and opposition-based module. The objective functions of the maximum entropy as well as the PSO are obtained, which have improved to optimize them and search the optimal threshold combination, to achieve multi-threshold image segmentation. The results demonstrate that the new algorithm improved the segmentation speed and enhanced the robustness. And the optimizing results are stable.

scholarly journals A image segmentation algorithm based on differential evolution particle swarm optimization fuzzy c-means clustering

2015 ◽  
Vol 12 (2) ◽  
pp. 873-893 ◽  
Author(s):  
Jiansheng Liu ◽  
Shangping Qiao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the proposed DEPSO-FCM has strong anti-noise ability; it can improve FCM and get better image segmentation results. In particular, for the HSI color image segmentation, the DEPSO-FCM can effectively solve the instability of FCM and the error split because of the singularity of the H component.

scholarly journals Different Object Functions for SWIPT Optimization by SADDE and APSO

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1340
Author(s):  
Wei Chien ◽  
Chien-Ching Chiu ◽  
Po-Hsiang Chen ◽  
Yu-Ting Cheng ◽  
Eng Hock Lim ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Antenna Array ◽  
Millimeter Wave ◽  
Bit Error Rate ◽  
Weighting Factor ◽  
Multiple Objective ◽  
The Novel ◽  
Object Function ◽  
Swarm Optimization ◽  
Multiple Receivers

Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case.

Optimal Design and Control of a Slider-Crank Mechanism System

2012 ◽  
Vol 487 ◽  
pp. 608-612 ◽  
Author(s):  
Chih Cheng Kao
Keyword(s):  
Particle Swarm Optimization ◽  
System Identification ◽  
Optimal Design ◽  
Fitness Function ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Pm Synchronous Motor ◽  
And Control ◽  
Crank Mechanism

This paper mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify a slider-crank mechanism driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider-crank mechanism. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. Finally, the comparisons of numerical simulations and experimental results prove that the MPSO identification method for the slider-crank mechanism is feasible.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

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