Optical fiber sensing based on Brillouin scattering spectrum [
1] has gained increasing attention in recent years. The traditional Brillouin scattering spectrum reveals a single peak only. Our previous study developed the genetic algorithm quantum-behaved particle swarm optimization (GA-QPSO) algorithm, which shows certain superiority in fitting single-peak Brillouin scattering spectrum [
2]. However, in long-distance optical fiber sensing systems, the metrical data of Brillouin scattering spectrum at certain points of optical fiber reveal multiple peaks [
3]. In special conditions, the multi-peak Brillouin spectrum is used to discriminate the intersecting sensitivity related to the changes in temperature and strain [
4]. An ideal feature extraction algorithm should accurately identify the amount and position of peaks in multi-peak spectrum and correctly plot the consecutive fitting curve. The feature extraction algorithm for single-peak spectrum is obviously unsuitable for multi-peak Brillouin scattering spectrum.