Modified genetic programming combining with particle swarm optimization and performance criterion in solar cell fabrication

2010 ◽  
Vol 8 (4) ◽  
pp. 841-849 ◽  
Author(s):  
Hyeon Bae ◽  
Tae-Ryong Jeon ◽  
Sungshin Kim ◽  
Seung-Soo Han
Keyword(s):  
Particle Swarm Optimization ◽  
Solar Cell ◽  
Genetic Programming ◽  
Particle Swarm ◽  
Performance Criterion ◽  
Swarm Optimization ◽  
Solar Cell Fabrication ◽  
Cell Fabrication ◽  
And Performance ◽  
Optimization And Performance

Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

2009 ◽  
Vol 14 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Hyeon Bae ◽  
Tae-Ryong Jeon ◽  
Sungshin Kim ◽  
Hyun-Soo Kim ◽  
Dongseop Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Solar Cell ◽  
Genetic Programming ◽  
Silicon Solar Cell ◽  
Solar Cell Fabrication ◽  
Cell Fabrication

The Assessment of Line-to-Plane Perpendicularity Error Based on Particle Swarm Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 913-918
Author(s):  
Jia Ding Bao ◽  
Rui Zhao ◽  
Bo Xu ◽  
Yong Hou Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Great Influence ◽  
Particle Swarm ◽  
Function Optimization ◽  
Multidimensional Space ◽  
Zone Method ◽  
Swarm Optimization ◽  
Space Function ◽  
Minimum Zone ◽  
And Performance

Perpendicularity error has great influence on the quality and performance of the geometrical products, and it is of great importance to guarantee the interchangeability. Particle swarm optimization (PSO) is an extraordinarily useful intelligent optimization algorithm with several advantages of fast convergence rate and easy realization for computer in the multidimensional space function optimization and dynamic target optimization. As a result, it is very accurate and is accordant with the requirement of minimum zone method (MZC) using PSO to associate the base plane of perpendicularity error.

Quantitative Feedback Theory control design using particle swarm optimization method

2011 ◽  
Vol 34 (4) ◽  
pp. 463-476 ◽  
Author(s):  
Hazem I Ali ◽  
Samsul Bahari B Mohd Noor ◽  
SM Bashi ◽  
Mohammad Hamiruce Marhaban
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Design Procedure ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Quantitative Feedback Theory ◽  
Swarm Optimization ◽  
Servo Actuator ◽  
Actuator System ◽  
And Performance

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller.

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