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为提高河流水质模型参数的求解精度,利用一种新的群体智能算法——狼群算法(WPA),对河流水质模型计算公式中的多参数进行同时优化,并与粒子群优化(PSO)算法及相关文献的优化结果进行比较。结果表明:WPA算法寻优精度优于PSO算法及其他相关算法,具有收敛速度快、稳定性能好和全局寻优能力强等优点。将WPA算法用于河流水质模型多参数寻优,可有效提高求解精度,避免了参数寻优结果变化范围过大的缺陷,为河流水质模型多参数寻优提供一种全新的方法和途径。
In order to improve the accuracy of the river water quality model parameters, a new swarm intelligence algorithm named wolf group algorithm (WPA) was used to simultaneously optimize the multi-parameters of the river water quality model calculation formula. The algorithm was also used in combination with Particle Swarm Optimization (PSO) And related literature optimization results were compared. The results show that the accuracy of WPA algorithm is better than that of PSO algorithm and other related algorithms, which has the advantages of fast convergence rate, good stability and global optimization ability. The WPA algorithm can be applied to multi-parameter optimization of river water quality model, which can effectively improve the accuracy of the solution and avoid the shortcomings of the variable range of parameter optimization results. It provides a new method and method for multi-parameter optimization of river water quality model.