遗传算法与自适应粒子群算法耦合的大坝安全预警评价模型

改变应用最小二乘法求解大坝统计预警模型的传统方式,利用粒子群算法随机搜索的优化能力确定统计模型的回归系数。针对粒子群算法收敛速度较慢等问题,提出一种新的自适应策略,能够依据粒子个体和种群的优化信息,调整学习因子,并将该策略与遗传算法的交叉、变异算子相结合。通过工程算例表明,该方法具备较好的搜索多样解能力,自适应地调整粒子飞行的步长,提高了粒子群算法的收敛速度;基于该方法的大坝预警评价模型与最小二乘法、基本粒子群算法相比,数据挖掘能力强,预警评价结果与大坝的实际运行状态更加吻合,有效地提高了统计模型的预测精度。

Safety early warning evaluation model for dams based on coupled method of genetic algorithm and adapting particle swarm optimization algorithm

To change the traditional method of applying the least square regression (LSM) in solving the statistical early warning model for dams, the stochastic search optimizing ability of particle swarm optimization (PSO) is employed to ascertain the regression coefficients of model. In order to solve the slow convergence rate of PSO used for a high-dimensional space optimization problem, a new self-adapting strategy that can adjust the learning factors, and combine with the crossover and mutation operators of genetic algorithm (GA) is proposed. The results show that the present method has better ability of searching diverse solutions and can adjust the flight length of particles by self-adapting, and can enhance the convergence rate of PSO; compared with the traditional least square regression and PSO, the data mining ability of this model is strong. The early warning evaluation results even more correspond with the practical operating condition, thus efficiently enhancing the forecasting precision of statistical models.