Decomposition of overlapping peaks in X-ray fluorescence using improved crow searching algorithm based on opposite learning

To overcome the difficulties in overlapping peaks decomposition in X-ray fluorescence (XRF) spectrum of heavy metal Pb and As, an improved crow searching algorithm (CSA) (ICSA) method was proposed. Compared with the CSA, three main improvements were performed: the opposite learning (OL) mechanism was introduced to expand the diversity of population and the parameter TF was introduced to control the convergence, which could avoid falling into the local minimum during the initialization process. Furthermore, this method has made some adjustments on the global optimization strategy, which can make the convergence more stable. When using the ICSA to decompose the overlapping peaks, the resolution accuracy improved by 2.23%, and the mean square error is better than other four algorithms of the same type, showing the feasibility of ICSA in dealing with the overlapping peaks.