Multiobjective Calibration of Reservoir Water Quality Modeling Using Multiobjective Particle Swarm Optimization (MOPSO)

Water resource management encounters large variety of multi objective problems that require powerful optimization tools in order to fully characterize the existing tradeoffs between various objectives that can be minimizing difference between forecasted physical, chemical, and biological behaviors of model and measured data. Calibration of complex water quality models for river and reservoir systems may include conflicting objectives addressed by various combinations of interacting calibration parameters. Calibration of the two dimensional CE-QUAL-W2 water quality and hydrodynamic model is an excellent example where the model must be calibrated for both hydrodynamic and water quality behavior. The aim of the present study is to show how multiobjective particle swarm optimization (MOPSO) can be implemented for automatic calibration of water quality and hydrodynamic parameters of a 2-dimensional, hydrodynamic, and water quality models (CEQUAL-W2) to predict physical, chemical, and biological behaviors of a water body, and then focus on a relevant case study. So MOPSO is utilized to generate Pareto optimal solutions for two conflicting calibration objectives. A combined measure of thermal and reservoir water level is considered as the first calibration objective. The second objective is formulated to forecast the best physical, chemical, and biological behavior of the model. Realizing the strong interactions between water quality and hydrodynamic issues of water bodies and their dependencies on the same set of calibration parameters, the proposed multiobjective approach may provide a wide version of all possible calibration solutions for better decision making to select best solution from pareto front.     

基于改进粒子群优化算法的新安江模型参数优选

新安江模型是一种实用有效的水文模型,在洪水预报以及水资源评估和管理中得到了广泛的应用。为此,结合新安江模型参数的特点,提出了基于改进粒子群优化算法的新安江模型参数优选方法,并将该模型应用到日径流预报中。实例表明,该方法能快速地完成参数寻优,并能较好地寻找出参数的全局最优解。     

改进粒子群优化算法在水文模型参数优化中的应用研究

引入改进的粒子群优化算法,对垂向混合产流模型计算参数进行优化,并对比参数优化前后水文模拟精度。研究结果表明：改进的粒子群优化算法模型可较快完成参数优化,相比于参数优化前,垂向混合产流模型年尺度模拟相对误差减少6.15%,模拟的过程确定性系数平均提高0.11;在次洪模拟中,模拟相对误差平均减少3.03%,模拟的洪水过程确定性系数平均提高0.19,水文模拟精度得到较大程度提高。研究成果对于区域水文模型参数优化提供参考价值。     

混沌粒子群优化算法在马斯京根模型参数优化中的应用

针对目前马斯京根模型参数率定中存在的求解复杂、精度不高等问题,本文将混沌搜索机制引入粒子群优化算法中,构建混沌粒子群优化算法对马斯京根模型参数进行率定。这种方法利用混沌运动的遍历性,改善了粒子群优化算法的全局寻优能力,避免算法陷入局部极值,使得粒子群体的进化速度加快,提高了算法的收敛速度和精度。通过实例应用表明,混沌粒子群优化算法可以有效地估算出马斯京根模型参数,优化效果明显优于粒子群优化算法及试错法,因此该算法具有很好的实用性。     

改进粒子群算法在梯级水库优化调度中的应用

针对粒子群优化算法搜索精度不高和易陷入局部最优解的缺陷,结合梯级水库联合优化调度的实际问题,提出了惯性权重的余弦处理机制和自适应选择学习对象策略,并结合分层交叉思想对粒子群算法进行了改进。实例计算表明:该算法合理、可靠,有效地提高了计算精度与效率。     

改进粒子群算法在新安江模型参数优选中的应用

粒子群优化算法的原理较易理解，所需参数较少而易于实现，但在具体问题中仍存在如收敛性不稳定等不足。基于收缩因子改进的粒子群算法可以保证算法的收敛性，同时使得速度的限制放松。结合新安江模型参数的特点，将其应用于该模型参数的率定．并编程实现检验分析。实例应用结果表明，该优化算法可得到较为稳定的模型参数．提高模型在洪水预报中的效率。     

Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization

This paper presents a methodology to achieve the identification of optimal hedging rules for operating reservoir systems, seeking to mitigate the drought impacts. The heuristic Particle Swarm Optimization (PSO) method is adopted as the optimization solver. This procedure establishes a two-phase method that combines PSO with the simulation of the water system, representing a system of reservoirs that are jointly operated to satisfy a set of demands with different priorities. The hedging rules are based on monthly storage levels that trigger restrictions on the demands. As model parameters, monthly rule activation thresholds and rationing factors were used for each type of demand. The optimization procedure minimizes an objective function that penalizes large deficits and assigns different weights to different demand types. Since the whole problem is quite complex, its dimensionality is reduced through: i) a set of candidate monthly activation thresholds are selected a priori associated to given risk conditions; and ii) the rationing factors are defined for every demand of each threshold throughout all months. In addition, an effort is made to avoid the trap in local optimums, whilst several other comments considering the application of the PSO method in the examined applications are provided. The procedure has been successfully applied to four water resource systems in Spain. From the application it can be seen that the deficits of the water supply demand are nearly removed, thanks to the larger weight given to the deficits of this demand type. The irrigation deficits are also reduced, since we lead to a sequence of smaller shortages than only one potential catastrophic shortage.     

基于改进粒子群算法的水库中长期调度函数研究

在应用改进粒子群算法求解水电站水库中长期优化调度问题时,为了克服基本粒子群存在早熟、后期收敛速度慢等不足,通过采用线性递减权重系数并引入杂交箅子,对基本PSO进行了改进,并将其应用于水库中长期优化调度函数研究中.与基于POA优化结果的回归分析建立的调度模型的对比分析表明,该算法原理简单,易于编程,求解精度高,从而为解决水库中长期优化调度问题提供了一种有效方法.     

Comparative Application of Model Predictive Control and Particle Swarm Optimization in Optimum Operation of a Large-Scale Water Transfer System

Water Resources Management - This research evaluates the application and performance of two methods of Model Predictive Control (MPC) and Particle Swarm Optimization (PSO) in real time control and...