Application of Heuristic Genetic Algorithm for Parameters Optimization of a Solar Cell Manufacturing Process

Abstract:

The more rapid development of economy, the more resources demand. The developments of solar and other alternative energy are particularly urgent and important. Diffusion manufacturing processes is core process of a solar cell manufacturing process. The physical and chemical reactions with the corresponding product characteristics are non-linear. The processes cannot be amended effectively depend on engineers experiences. In order to find the optimal process parameters for the silicon solar cell diffusion process, this research proposed an approach which combines several methods: Revised Multi-objective genetic algorithms (RMOGA) and Adaptive Multi-objective genetic algorithms (AMOGA) that integrates back-propagation neural networks (BPN), technique for order preference by similarity to ideal solution (TOPSIS), and genetic algorithms (GA) with the concept of elite sets and local search. The result of this study shows that AMOGA has the best performance to enhance the breadth and depth of the MOGA search, and also quickly convergent to the high quality and quantity optimal solutions. That provides decision-makers more diverse choices.

• Periodical: Advanced Materials Research (Volume 711)
• Pages: 758-762
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.711.758
• Online since: June 2013
• Authors: Tung Hsu Hou, Cecilia Lin, Keng Yu Lin
• Keywords: Back-Propagation Neural Network, Diffusion, Multiple Objective Genetic Algorithm

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