Automatic Tuning Method for Quadrupole Mass Spectrometer Based on Improved Differential Evolution Algorithm

TL;DR

This paper introduces an improved algorithm for automatically tuning quadrupole mass spectrometers, achieving better performance than traditional methods.

Contribution

An improved differential evolution algorithm with ranking and subpopulation classification for automatic tuning of mass spectrometers.

Findings

The improved algorithm outperformed the univariate search method by 25.3% in tuning performance.

It surpassed classical differential evolution and standard particle swarm optimization algorithms in experiments.

Validation on CEC-2017 benchmark functions confirmed the algorithm's superiority.

Abstract

Quadrupole mass spectrometers are highly sensitive and specific analytical instruments, widely used in pharmaceuticals, clinical diagnostics, and other fields. Their performance depends on a tuning process to optimize key parameters, which has traditionally relied on engineers’ expertise or simple univariate search methods. This paper proposes an automatic tuning method using an improved differential evolution algorithm. This algorithm introduces a ranking and subpopulation classification for individuals, enabling distinct mutation strategies. Validation on the CEC-2017 benchmark functions confirms the superiority of the improved algorithm. In automatic tuning experiments, it achieved a 25.3% performance gain over the univariate search method and also surpassed both the classical differential evolution algorithm and standard particle swarm optimization algorithm. This method proves to be an effective approach for enhancing the performance of quadrupole mass spectrometers.

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