Retrieval of land surface parameters in the Sahel from ERS wind scatterometer data: a "brute force" method

2002 ◽  
Vol 40 (9) ◽  
pp. 2056-2062 ◽  
Author(s):  
L. Jarlan ◽  
P. Mazzega ◽  
E. Mougin
Keyword(s):  
Land Surface ◽  
Brute Force ◽  
Surface Parameters ◽  
Force Method ◽  
Brute Force Method ◽  
Land Surface Parameters

Retrieving land surface parameters over Sahel from ERS wind scatterometer data

2001 ◽  
Author(s):  
Lionel Jarlan ◽  
Pierre Mazzega ◽  
Eric Mougin ◽  
Pierre L. Frison
Keyword(s):  
Land Surface ◽  
Surface Parameters ◽  
Land Surface Parameters

scholarly journals Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1726
Author(s):  
Premkumar Vincent ◽  
Gwenaelle Cunha Sergio ◽  
Jaewon Jang ◽  
In Man Kang ◽  
Jaehoon Park ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Solar Cells ◽  
Heuristic Method ◽  
Brute Force ◽  
Force Parameter ◽  
Thickness Optimization ◽  
Sweep Method ◽  
Case Scenario ◽  
Force Method ◽  
Brute Force Method

Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method.

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