Texture decomposition with particle swarm optimization method
作者： Jian-Guo Tang , Xin-Mingm Zhang , Yun-Lai Deng , Yu-Xuan Du , Zhi-Yong Chen
DOI: 10.1016/J.COMMATSCI.2005.09.015
关键词: Swarm behaviour 、 Decomposition (computer science) 、 Algorithm 、 Evaluation result 、 Multi-swarm optimization 、 Linear correlation 、 Texture (geology) 、 Computer science 、 Particle swarm optimization 、 Simplex
摘要: Abstract The newly developed optimization algorithm-particle swarm (PSO) algorithm is introduced into the crystallographic texture decomposition. With linear correlation factor as evaluation parameter, both PSO and Nelder–Mead Simplex (NMS) are evaluated in this paper. result reveals that more effective when it comes to complicated multi-component textures, i.e., instead of falling local minimum NMS algorithm, goes global minimum. So high quality decomposition obtained with algorithm.
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