Influence of parameter settings in PSO Algorithm on simulation results of Xin'anjiang model
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Abstract:
The reaso nable parameter settings in the particle swarm optimization algorithm canimprove the optimization efficiency and avoidfalling into the local optimum. However, common parameter settings are not universally applicable to specific optimization problems, such as the simulation of Xin'anjiang model. In this study, we conducted orthogonal tests to study the influence of 5 particle swarm parameters on the simulat ionresults of Xin’anjiang model. Through the analysis of the test results, we revealed the influence of parameters on the performance of PSO algorithm and obtained the optimum parameters ( pop = 80, w = linear regression from 1.3 to 0.4, c? = 1. 85, c? = 2. 5, m= 0. 05) . Through range analysis and variance analysis, we found that the parameters pop and warehighly significant to the simulation results, and the other three parameters are not significant to the simulation results. The different PSO parameter sets were applied to Xin’anjiang model simulation, and proved that the reasonable PSO algorithm parameter setting can effectively improvet he simulation accuracy of Xin'anjiang model. Through the trend analysis of each factor, we obtained the relationship between the change trend of the factor value and the change trend of the model result. The method presented in this paper can providereference for finding the parameters of PSO algorithm in a specific application scenario.
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