A Novel Symmetrical Peak Fitting Method Based on Improved WOA Algorithm for the Analysis of Microchip Electrophoresis Signals

The problem of overlapping peaks has been a challenge in microchip electrophoresis (ME) signal analysis. However, traditional peak fitting algorithms have difficulty analyzing overlapping peaks due to the high dependence on the starting point. In this study, we propose a symmetrical peak fitting method named the tent-mapped whale optimization algorithm and Levenberg–Marquardt (TWOALM), which combines a whale optimization algorithm (WOA) improved by one of the most commonly used chaotic maps, the tent map and the Levenberg–Marquardt (LM) algorithm. Specifically, we first derive the fitted model for the overlapping peaks, showing that it is separable nonlinear least squares, significantly reducing the number of parameters to be optimized. Second, we integrate the tent map into the WOA, which improves the convergence speed of the peak fitting algorithm. Finally, we propose an efficient peak-fitting algorithm that combines the improved WOA and LM. The advantage of the proposed algorithm is that it is significantly faster than WOA and significantly more accurate than the LM algorithm. The results of fitting the synthetic peaks and ME signals showed that the combination of the chaotic map-based WOA algorithm and the LM algorithm can significantly improve the peak fitting performance and provide an effective solution for the analysis of overlapping peaks.