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Journal of System Simulation

Abstract

Abstract: High-precision micro-population data are one of the key basic data for simulation systems such as disease spread, traffic travel, and emergency events. In reality, computer-generated artificial populations are often used for simulation. Due to computational efficiency and standardization of generation steps, the iterative proportional fitting method is currently used for artificial population synthesis. However, it has strict requirements on basic data and faces zero-unit and data representational deviation problems, and it fails to guarantee the fitting at the individual and family levels at the same time. In order to overcome this deficiency, an improved genetic algorithm using a simulated annealing algorithm to generate an initial solution and elitist selection strategy is proposed to synthesize artificial populations and explore the synthetic steps and parameter setting of the genetic algorithm. Based on the generated artificial population in Chengdu, a simulation experiment of biological aerosol diffusion is carried out. Experiments show that the accuracy of artificial populations generated by the genetic algorithm is better than that generated by traditional methods and has low requirements for basic data. This data set can provide fine-scale multi-attribute population information for simulation research.

First Page

1965

Last Page

1974

CLC

TP391.9

Recommended Citation

Zhang Hongli, Deng Jingshuang. Research on Artificial Population Generation and Application Based on Genetic Algorithm[J]. Journal of System Simulation, 2023, 35(9): 1965-1974.

DOI

10.16182/j.issn1004731x.joss.22-0525

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