Safety early warning evaluation model for dams based on coupled method of genetic algorithm and adapting particle swarm optimization algorithm
WANG Wei 1, SHEN Zhenzhong 1, LI Taofan 2
1. College of Water Conservancy and Hydropower Engineering Hohai University Nanjing 210098 China 2. No.8 Bureau of Hydraulic and Hydroelectric Engineering of China Changsha 410007 China
Abstract：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.
王伟；沈振中；李桃凡；. 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[J]. 岩土工程学报, 2009, 31(8): 1242-1247.
WANG Wei 1, SHEN Zhenzhong 1, LI Taofan 2. Safety early warning evaluation model for dams based on coupled method of genetic algorithm and adapting particle swarm optimization algorithm. Chinese J. Geot. Eng., 2009, 31(8): 1242-1247.
