A metric to assist the selection of the particle swarm optimization parameters

2014 ◽  
pp. 105-109 ◽  
Author(s):  
C da Silva ◽  
W Saba ◽  
N Abe ◽  
A Passaro
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimization Parameters ◽  
Selection Of

scholarly journals Power load forecasting algorithm based on nonlinear inertial factor change pattern particle swarm optimization algorithm

2018 ◽  
Vol 173 ◽  
pp. 02016
Author(s):  
Jin Liang ◽  
Wang Yongzhi ◽  
Bao Xiaodong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Power Load ◽  
Selection Of

The common method of power load forecasting is the least squares support vector machine, but this method is very dependent on the selection of parameters. Particle swarm optimization algorithm is an algorithm suitable for optimizing the selection of support vector parameters, but it is easy to fall into the local optimum. In this paper, we propose a new particle swarm optimization algorithm, it uses non-linear inertial factor change that is used to optimize the algorithm least squares support vector machine to avoid falling into the local optimum. It aims to make the prediction accuracy of the algorithm reach the highest. The experimental results show this method is correct and effective.

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