Research on Site Selection of Aviation Overhaul Depot Based on Particle Swarm Optimization

2013 ◽  
Vol 274 ◽  
pp. 620-623
Author(s):  
Cheng Liang Liu ◽  
Hui Lin Fan ◽  
Man Yi Hou ◽  
Xue Liang Bao
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Programming ◽  
Support System ◽  
Site Selection ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Swarm Optimization ◽  
Aviation Maintenance ◽  
Nonlinear Programming Method ◽  
Selection Of

As support agencies of the aviation maintenance and support system, the aviation overhaul depots undertake an important and arduous support mission, which play a pivotal role, so the science and rationality of site selection is extremely significant. This paper choose the nonlinear programming method to build a mathematic model, so as to solve the problem. For the purpose of optimizing and solving the model, a thought based on the improved particle swarm optimization which used in the model is put forward to, and an improved PSO which gained by the analyzing the standard PSO and improving the initial PSO is presented in this paper. Finally, an application example is given to analyze and summarize the model.

A Novel Control Scheme for PWM Boost Converter based on Sliding Mode Control using Particle Swarm Optimization

2020 ◽  
Vol 14 ◽  
Author(s):  
Gang Liu ◽  
Dong Qiu ◽  
Xiuru Wang ◽  
Ke Zhang ◽  
Huafeng Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Absolute Error ◽  
Boost Converter ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Steady State Performance

Background: The PWM Boost converter is a strongly nonlinear discrete system, especially when the input voltage or load varies widely, therefore, tuning the control parameters of which is a challenge work. Objective: In order to overcome the issues, particle swarm optimization (PSO) is employed for tuning the parameters of a sliding mode controller of a boost converter. Methods: Based on the analysis of the Boost converter model and its non-linear characteristics, a mathematic model of a boost converter with a sliding mode controller is built firstly. Then, the parameters of the Boost controller are adjusted based on the integrated time and absolute error (ITAE), integral square error (ISE) and integrated absolute error (IAE) indexes by PSO. Results: Simulation verification was performed, and the results show that the controllers tuned by the three indexes all have excellent robust stability. Conclusion: The controllers tuned by ITAE and ISE indexes have excellent steady-state performance, but the overshoot is large during the startup. The controller tuned by IAE index has better startup performance and slightly worse steady-state performance.

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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