Mutual-inclusive learning-based multi-swarm PSO algorithm for image segmentation using an innovative objective function

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Madan Garg ◽  
Rama Sushil ◽  
Rupak Chakraborty
Keyword(s):  
Image Segmentation ◽  
Objective Function ◽  
Pso Algorithm ◽  
Inclusive Learning

Missile Guidance Algorithm Design Using Particle Swarm Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 2411-2415
Author(s):  
Chien Chun Kung ◽  
Kuei Yi Chen
Keyword(s):  
Objective Function ◽  
Fitness Function ◽  
Equations Of Motion ◽  
Algorithm Design ◽  
Solution Space ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Missile Guidance ◽  
Pursuit Evasion ◽  
Guidance Algorithm

This paper presents a technique to design a PSO guidance algorithm for the nonlinear and dynamic pursuit-evasion optimization problem. In the PSO guidance algorithm, the particle positions of the swarm are initialized randomly within the guidance command solution space. With the particle positions to be guidance commands, we predict and record missiles’ behavior by solving point-mass equations of motion during a defined short-range period. Taking relative distance to be the objective function, the fitness function is then evaluated according to the objective function. As the PSO algorithm proceeds, these guidance commands will migrate to a local optimum until the global optimum is reached. This paper implements the PSO guidance algorithm in two pursuit-evasion scenarios and the simulation results show that the proposed design technique is able to generate a missile guidance law which has satisfied performance in execution time, terminal miss distance, time of interception and robust pursuit capability.

scholarly journals Recursive Neural Networks Based on PSO for Image Parsing

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
State Of The Art ◽  
Pso Algorithm ◽  
Training Algorithm ◽  
Image Parsing ◽  
Swarm Optimization ◽  
Recursive Neural Networks ◽  
Parsing Algorithm

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

Image Segmentation Based on Least Squares Support Vector Machines

2014 ◽  
Vol 644-650 ◽  
pp. 4314-4318
Author(s):  
Xin You Wang ◽  
Ya Li Ning ◽  
Xi Ping He
Keyword(s):  
Image Segmentation ◽  
Support Vector Machines ◽  
Least Squares ◽  
Color Image ◽  
Pso Algorithm ◽  
Kernel Functions ◽  
Color Image Segmentation ◽  
Support Vector ◽  
Standard Data ◽  
Vector Machines

In order to solve the problem of the conventional methods operated directly in the image, difficult to obtain good results because they are poor in high dimension performance. In this paper, a new method was proposed, which use the Least Squares Support Vector Machines in image segmentation. Furthermore, the parameters of kernel functions are also be optimized by Particle Swarm Optimization (PSO) algorithm. The practical application in various of standard data sets and color image segmentation experiment. The results show that, LS-SVM can use a variety of features in image, the experiments have achieved good results of image segmentation, and the time needed for segmentation is greatly reduced than standard SVM.

scholarly journals Toward the Optimal Operation of Hybrid Renewable Energy Resources in Microgrids

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5482
Author(s):  
Shabir Ahmad ◽  
Israr Ullah ◽  
Faisal Jamil ◽  
DoHyeun Kim
Keyword(s):  
Renewable Energy ◽  
Objective Function ◽  
Renewable Resources ◽  
Renewable Energy Sources ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Energy Resources ◽  
Energy Sources ◽  
Renewable Energy Resources ◽  
Cost Efficient

Renewable energy sources are environmentally friendly and cost-efficient. However, the problem with these renewable resources is their heavy reliance on weather conditions. Thus, at times, these solutions are not guaranteed to meet the required demand all the time. For this, hybrid microgrids are introduced, which have a combination of both renewable energy sources and non-renewable energy resources. In this paper, a cost-efficient optimization algorithm is proposed that minimizes the use of non-renewable energy sources. It maximizes the use of renewable energy resources by meeting the demand for utility grids. Real data based on the load and demand of the utility grids in Italy is used, and a system that determines the optimal sizing of the microgrid and a daily plan is introduced to optimize the renewable resources operations. As part of the proposal, the objective function for the operation and planning of the microgrid in such a way to minimize cost is formulated. Moreover, a variant of the PSO algorithm named recurrent PSO is implemented. The recurrent PSO algorithm solves the proposed optimization objective function by minimizing the cost for the installation and working of the microgrid. Afterwards, the energy management system algorithm lays out a plan for the daily operation of the microgrid. The performance of the system is evaluated using different state-of-the-art optimization methods. The proposed work can help minimize the use of diesel generators, which not only saves financial resources but also contributes toward a green environment.

A unified optimal power flow modeling for VSC-HVDC converter: a novel methodology for optimal installation based on average loadability index

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sunilkumar Agrawal ◽  
Prasanta Kundu
Keyword(s):  
Objective Function ◽  
Direct Current ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Pso Algorithm ◽  
Transmission Network ◽  
Content Type ◽  
Hvdc Transmission ◽  
Optimal Power ◽  
The Impact

Purpose This paper aims to propose a novel methodology for optimal voltage source converter (VSC) station installation in hybrid alternating current (AC)/direct current (DC) transmission networks. Design/methodology/approach In this analysis, a unified power flow model has been developed for the optimal power flow (OPF) problem for VSC-based high voltage direct current (VSC-HVDC) transmission network and solved using a particle swarm optimization (PSO) algorithm. The impact of the HVDC converter under abnormal conditions considering N-1 line outage contingency is analyzed against the congestion relief of the overall transmission network. The average loadability index is used as a severity indicator and minimized along with overall transmission line losses by replacing each AC line with an HVDC line independently. Findings The developed unified OPF (UOPF) model converged successfully with (PSO) algorithm. The OPF problem has satisfied the defined operational constraints of the power system, and comparative results are obtained for objective function with different HVDC test configurations represented in the paper. In addition, the impact of VSC converter location is determined on objective function value. Originality/value A novel methodology has been developed for the optimal installation of the converter station for the point-to-point configuration of HVDC transmission. The developed unified OPF model and methodology for selecting the AC bus for converter installation has effectively reduced congestion in transmission lines under single line outage contingency.

The Improved Membership Matrix Initialization Method of FCM for Image Segmentation

2014 ◽  
Vol 989-994 ◽  
pp. 3743-3746
Author(s):  
Zong Jia Wu ◽  
Li Kun Liu
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Objective Function ◽  
Membership Function ◽  
Original Image ◽  
Optimum Point ◽  
Blurred Image ◽  
Membership Matrix

The performance of FCM for image segmentation directly subjects to the initialized membership matrix. This paper proposed twice FCM method to solve the membership matrix initiation problem. The image is spared to a blurred image at first, and then uses the FCM for the blurred image to obtain an iterative result, in which the membership matrix is taken as the initialized membership function of the FCM for the original image processing. This method overcomes the random membership initialization method cannot convergence to the optimum point of the objective function of FCM for the image segmentation at some extend, furthermore, it can obtain better results than the traditional FCM method.

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