Test Point Selection Method Research Based on Genetic Algorithm and Binary Particle Swarm Optimization Algorithm

2015 ◽  
pp. 577-585 ◽  
Author(s):  
Xiaofeng Lv ◽  
Ling Ma ◽  
Jing Sun ◽  
Wei Pang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Selection Method ◽  
Test Point ◽  
Binary Particle Swarm Optimization ◽  
Point Selection ◽  
Swarm Optimization ◽  
Test Point Selection

scholarly journals Binary Particle Swarm Optimization Algorithm for Kidney Exchanges

2020 ◽  
Author(s):  
Wael M. F. Abdel-Rehim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Kidney Exchange ◽  
To Receive

In this paper, we implement a Particle Swarm Optimization (PSO) based method for solving the kidney exchange problem. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Implementing Particle Swarm Optimization in solving the kidney exchange problem is an effective method because the obtained results indicate that PSO outperforms other stochastic-based methods such as Genetic Algorithm regarding the efficient the number of resulting exchanges.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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