Application of the particle swarm optimization method for the analysis of wide-angle X-ray diffraction curves of semicrystalline polymers

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• 作者：Małgorzata Rabiej
• 关键词：X-ray diffraction curves ; artificial intelligence ; particle swarm optimization ; multi-criterial optimization
• 刊名：Journal of Applied Crystallography
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：50
• 期：1
• 页码：221-230
• 全文大小：1197K
• ISSN：1600-5767

文摘

The analysis of wide-angle X-ray diffraction curves of semicrystalline polymers is connected with a thorough decomposition of these curves into crystalline peaks and amorphous components. A reliable and unambiguous decomposition is the most important step in calculation of the crystallinity of polymers. This work presents a new algorithm dedicated to this aim, which is based on the particle swarm optimization (PSO) method. The PSO method is one of the most effective optimization techniques that employs a random choice as a tool for going through the solution space and searching for the global solution. The action of the PSO algorithm imitates the behaviour of a bird flock or a fish school. In the system elaborated in this work the original PSO algorithm has been equipped with several heuristics. The role of heuristics is performed by procedures which orient the search of the solution space using additional information. In this paper it is shown that this algorithm is faster to converge and more efficiently performs a multi-criterial optimization compared with other algorithms used for this purpose to date.