|
|||||
|
|
Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements |
|
| Institution: | 1. Jiangsu Key Laboratory of Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing 211189, China;2. Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;3. Railway Engineering Institute, China Academy of Railway Science, Beijing 100081, China;4. State Key Laboratory for Track Technology of High-Speed Railway, Beijing 100081, China |
| 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. |
| Keywords: | Quantum-behaved particle swarm optimization Aerosol Aerosol size distribution Inverse problem ACO"} {"#name":"keyword" "$":{"id":"kw0030"} "$$":[{"#name":"text" "_":"ant colony optimization ADA"} {"#name":"keyword" "$":{"id":"kw0040"} "$$":[{"#name":"text" "_":"anomalous diffraction approximation ARPSO"} {"#name":"keyword" "$":{"id":"kw0050"} "$$":[{"#name":"text" "_":"attractive and repulsive particle swarm optimization ASD"} {"#name":"keyword" "$":{"id":"kw0060"} "$$":[{"#name":"text" "_":"aerosol size distribution DE"} {"#name":"keyword" "$":{"id":"kw0070"} "$$":[{"#name":"text" "_":"differential evolution FOA"} {"#name":"keyword" "$":{"id":"kw0080"} "$$":[{"#name":"text" "_":"fruit fly optimization algorithm GA"} {"#name":"keyword" "$":{"id":"kw0090"} "$$":[{"#name":"text" "_":"genetic algorithm GCV"} {"#name":"keyword" "$":{"id":"kw0100"} "$$":[{"#name":"text" "_":"generalized cross-validation regularization method PSD"} {"#name":"keyword" "$":{"id":"kw0110"} "$$":[{"#name":"text" "_":"particle size distribution PSO"} {"#name":"keyword" "$":{"id":"kw0120"} "$$":[{"#name":"text" "_":"particle swarm optimization QPSO"} {"#name":"keyword" "$":{"id":"kw0130"} "$$":[{"#name":"text" "_":"quantum-behaved PSO SPSO"} {"#name":"keyword" "$":{"id":"kw0140"} "$$":[{"#name":"text" "_":"stochastic PSO |
| 本文献已被 ScienceDirect 等数据库收录！ | |