基于MOPSO算法的垂向混合产流模型参数多目标优化

Multi-objective Optimization of Parameters of Vertical Mixed Runoff Model Based on MOPSO Algorithm

针对水文过程模拟模型的复杂性及校准参数的准确性问题,提出一种相对较新的全局优化算法——多目标粒子群优化(MOPSO)算法定义多个目标函数,并引入Pareto前沿解概念,扩大了采样空间,提高了算法的运算速度和鲁棒性,进而对垂向混合产流模型进行模拟。实例应用结果表明,与单目标算法相比,多目标算法能较准确地模拟水文过程的多方面因素,提高了优化结果的精度。

Aiming at the complexity of the simulation model of hydrological process and the accuracy of calibration parameters,a relatively new global optimization algorithm,multi objective particle swarm optimization(MOPSO)algorithm,was proposed,which defines multiple objective functions and introduces the concept of Pareto frontier solution.The proposed method expands the sampling space and improves the computational speed and robustness of the algorithm.And then,the vertical mixed runoff model is simulated.The application results show that compared with the single objective algorithm,the multi objective algorithm can accurately simulate many aspects of the hydrological process and improve the accuracy of the optimization results.