Simplex-particle swarm algorithm for parameter estimation in two-dimensional water quality model of river
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Abstract:
Simplex-particle swarm hybrid algorithm (SM-PSO) was applied to analyze the experimental data of water quality of river in two-dimensional transverse dispersion, and to estimate the transverse dispersion coefficient, mean velocity of river, and location of continuous pollutant discharge. The results of numerical experiment show that: 1) SM-PSO algorithm can be effectively employed to analyze the experimental data of water quality and estimate water quality parameters. 2) Under the same condition, the time performance indicator of SM-PSO is less than that of PSO algorithm. 3) The range of initial guess value of water quality parameters has little influence on the convergence speed . 4) c1, c2 and the range of initial guess value have synthetic influences on the search capability in operation. When c1=c2=1.72, the search capability can be kept properly. SM-PSO algorithm can overcome the problem of PSO algorithm where it easily drops into local convergence and premature convergence. The hybrid algorithm was proved to be an effective way to estimate parameters for river water quality models.
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