Comparative Study of Impacts of Typical Bio-Inspired Optimization Algorithms on Source Inversion Performance - Mao, Shushuai; Hu, Feng; Lang, Jianlei; Chen, Tian; Cheng, Shuiyuan (Frontiers in Environmental Science, 2022)
Accurate identification of source emission information (i.e., source strength and location) is crucial for the air pollution refined control or effective accidental response. Optimization inversion based on bio-inspired algorithms (BIOs) is an effective method for estimating source information. However, the impacts of different BIOs and the shared parameter of population size in BIOs on source inversion performance have not been revealed. To fill the gap, the source inversion performance (i.e., accuracy and robustness) of six typical BIOs (i.e., bacterial foraging optimization algorithm [BFO], chicken swarm optimization algorithm [CSO], differential evolution algorithm [DE], genetic algorithm [GA], particle swarm optimization [PSO], and seeker optimization algorithm [SOA]), and their population sizes are evaluated based on the Prairie Grass experiment dataset. The inversion performances of all BIOs were analyzed for unknown source strength and location parameters under different atmospheric conditions (i.e., Pasquill stability classes A, B, C, D, E, and F). Results indicated the population size of each test algorithm has substantial influence on source inversion accuracy and robustness. The population sizes required to reach stable source inversion were 20, 30, 50, 30, 30, and 10 for the BFO, CSO, DE, GA, PSO, and SOA, respectively. Overall, the BFO had the best accuracy in source strength and locations, whereas the SOA had the best robustness. Atmospheric dispersion conditions indicated an obvious influence on the source inversion performance of different BIOs. Comprehensive evaluation scores based on accuracy and robustness revealed that the DE, GA, and SOA performed best, with the lowest comprehensive performance scores under stability A, B, and C-F, respectively. This study enhances the understanding of the factors inﬂuencing source inversion and provides a reference for the more reasonable selection of appropriate bio-inspired algorithms and setting of population size parameter in algorithm for source inversion in practical environmental management.