Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements--颗粒学报PARTICUOLOGY

He, Z., Qi, H., Chen, Q., & Ruan, L. (2016). Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements. Particuology, 28, 6-14. https://doi.org/10.1016/j.partic.2014.12.016

Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements

Zhenzong He, Hong Qi ^*, Qin Chen, Liming Ruan

School of Energy Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China

10.1016/j.partic.2014.12.016

Volume 28, October 2016, Pages 6-14

Received 20 October 2014, Revised 29 December 2014, Accepted 31 December 2014, Available online 7 July 2015, Version of Record 4 August 2016.

E-mail: qihong@hit.edu.cn

Highlights

• Improved quantum-behaved particle swarm optimization (IQPSO) method was employed to determine ASD.

• Size distributions of various aerosol types were estimated under dependent and independent models.

• Four wavelengths and 50 particles were recommended to be optimization parameters for IQPSO.

• The IQPSO showed higher convergence speed and accuracy than other PSO methods.

Abstract

An improved quantum-behaved particle swarm optimization (IQPSO) algorithm is employed to determine aerosol size distribution (ASD). The direct problem is solved using the anomalous diffraction approximation and Lambert–Beer's Law. Compared with the standard particle swarm optimization algorithm, the stochastic particle size optimization algorithm and the original QPSO, our IQPSO has faster convergence speed and higher accuracy within a smaller number of generations. Optimization parameters for the IQPSO were also evaluated; we recommend using four measurement wavelengths and 50 particles. Size distributions of various aerosol types were estimated using the IQPSO under dependent and independent models. Finally, experimental ASDs at different locations in Harbin were recovered using the IQPSO. All our results confirm that the IQPSO algorithm is an effective and reliable technique for estimating ASD.

Graphical abstract

Keywords

Quantum-behaved particle swarm optimization; Aerosol; Aerosol size distribution; Inverse problem

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