Multi-Objective Differential Evolution-Chaos Shuffled Frog Leaping Algorithm for Water Resources System Optimization

A multi-objective differential evolution-chaos shuffled frog leaping algorithm (MODE-CSFLA) is proposed for water resources system optimization to overcome the shortcomings of easily falling into local minima and premature convergence in SFLA. The performance of MODE-CSFLA in solving benchmark problems is compared with that of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). At last, the proposed MODE-CSFLA is used to optimize the water resources allocation plan of the East Route of the South-to-North Water Transfer Project in the normal, dry, and extremely dry years. The results reveal that MODE-CSFLA performs better than NSGA-II and MOPSO under all conditions. Compared with shuffled frog leaping algorithm (SFLA), MODE-CSFLA can result in a 29.39, 27.47 and 22.55% increase in water supply when the single objective is to minimize the water pumpage; and a 41.01, 39.63 and 30.94% decrease in total pumpage when the single objective is to maximize the water supply in the normal, dry, and extremely dry conditions, respectively. Thus, MODE-CSFLA has the potential to be used for solving complex optimization problems of water resources systems.     

差分进化算法在马斯京根模型参数优选中的应用

针对马斯京根模型参数最优估计中求解复杂、精度差等问题,结合绝对残差绝对值之和最小准则,提出应用差分进化算法（Differential Evolution Algorithm）直接优选模型参数。同其它算法相比,实例分析表明该算法具有较强的全局搜索能力和较高的计算精度。为更好地优选马斯京根模型参数提供了一种更为有效的新方法。     

Multi-Objective Parameter Calibration and Multi-Attribute Decision-Making: An Application to Conceptual Hydrological Model Calibration

For calibrating the conceptual hydrological models (CHM), the traditional calibration method with a single objective cannot properly measure all the behaviors of the hydrological system. To obtain a successful parameters calibration, in this paper, we propose a multi-objective cultural self-adaptive electromagnetism-like mechanism (MOCSEM) algorithm, which is first implemented in solving the parameters calibration problem of CHM. In this algorithm, a self-adaptive parameter is applied in local search operation for adjusting the values of parameters dynamically. Meanwhile, cultural algorithm (CA) is adopted to keep a good diversity and uniformity of Pareto-optimal solutions (POS). MOCSEM is tested, firstly, by several benchmark test problems. After achieving satisfactory performance on the test problems, a case study is implemented for parameter calibration of a CHM by comparing the properties of POS obtained by the MOCSEM and other methods. Finally, when the optimization problem quickly becomes a decision-making problem because of the multiple objectives in CHM, fuzzy technique for order preference by similarity to an ideal solution method has been used to rank the POS and select the optimal scheme. The results show that the MOCSEM algorithm can provide high-accuracy parameters of CHM on various decision-making scenarios.     

遗传算法在水箱径流模型参数优选中的应用

水文模型参数优选是水文模型研究中的重点和难点,应用传统的基于梯度下降、导数理论的优化算法难以取得较好效果。遗传算法是一种多参数、多个体全局智能优化算法,在参数优选中应用广泛且效果较好。将遗传算法应用于水箱年径流模型参数优化中,通过实例应用表明,遗传算法较传统优化算法在模型参数优选中收敛速度、成果精度等方面有所提升,效果较好。     

松华坝水资源安全评价的可变模糊识别方法

水资源作为社会经济可持续发展的重要因素,其安全问题受到越来越多的重视。将可变模糊识别方法引用到松华坝水源保护区水资源安全评价中,并以二元比较法确定权重,得到的评价结果是,松华坝水源保护区的水资源处于基本安全状态。这与其他文献中用层次分析法、集对分析法等方法的评价结果基本一致,表明可变模糊识别方法能够有效地运用于水资源安全多指标综合评价中。     

A Novel Parallel Cellular Automata Algorithm for Multi-Objective Reservoir Operation Optimization

Water Resources Management - In this paper, a novel Parallel Cellular Automata (PCA) approach is presented for multi-objective reservoir operation optimization. The problem considers the...     

基于MOSCEM-UA算法的水文模型参数优化

详细阐述了水文模型参数多目标自动优化过程,并以此来评估水文模型在率定过程中所体现出来的异参同效性。采用集总式Hymod模型研究海河流域宽城水文站以上流域。在模型参数优化率定过程中涉及到的主要模型参数有:土壤最大持水量,土壤持水量空间分布指数,快、慢速流分水系数以及快、慢速流退水系数。采用了两个对立目标,通过MOSCEM-UA算法的多目标参数优化,可以得到多个目标空间的Pareto解集、径流预测区间和相对应的参数范围。     

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。     

混沌差分进化算法在水库优化调度中的应用研究

针对差分进化算法在求解水库调度等复杂优化问题时,算法初始种群的随机性导致其在解空间中的代表性不足,算法的贪婪选择策略又极易导致种群迅速趋同而"早熟"收敛。提出初始种群的混沌生成策略,利用混沌因子的遍历性提高算法初始种群的代表性。同时,以动态概率接受适应值较差的个体作为子代个体参与进化,从而提高算法跳出局部最优解的能力。将改进的差分进化算法模拟乌江梯级电站优化调度问题,模拟计算结果表明,改进的差分进化算法具有较高全局搜索能力,大幅提高了求解的精度,适合求解水库优化调度等问题。     

改进差分进化算法在水库优化调度中的应用研究

差分进化算法在求解水库优化调度时,进化后期种群多样性急剧下降,导致算法无法跳出局部最优解而出现“早熟”收敛。针对该问题,该文对算法的贪婪选择策略进行改进,使其以一定的概率动态接受稍差解作为子代个体,从而提高算法的种群多样性;同时,提出种群基因重生策略,进一步改善种群进化的基因信息结构。将改进的差分进化算法应用于清江梯级发电调度问题,并与差分进化算法、模拟退火算法求解结果进行对比。模拟结果表明,改进算法具有更强的全局搜索能力,求解梯级水库优化调度问题更具有优势。     

遗传算法在三水源新安江模型参数率定中的应用研究

遗传算法是一种全局数值优化方法。该文在三水源新安江模型的参数率定问题中,结合遗传算法的基本原理和参数自身的特点,提出了基于遗传算法的参数率定方法,并通过实验确定遗传算法的参数。经过清江流域几次洪水历史资料的实例应用验证,结果表明该方法直观、简便、适用性强,率定的模型参数是合理的,能够提高洪水预报精度。该文所介绍的方案已成功应用于“数字清江”工程洪水预报中。     

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。     

分布式水文模型EasyDHM在海河阜平流域的应用

目前很多分布式水文模型还主要应用在科研领域,缺乏在实际的水管理业务中的应用。分布式水文模型EasyDHM的开发则以实用为目的,着重关注模拟效果及建模效率。模型具有友好的操作界面,适用于各种时空尺度的流域水文模拟,并且模型易于扩展,可进行水资源评价。以海河流域阜平水文站以上流域为研究对象,应用EasyDHM模型进行流域水文模拟,通过对模型参数进行敏感性分析及率定,使阜平流域率定期及验证期Nash效率系数达到0．96。分析表明,EasyDHM模型在阜平流域具有很好的适用性。     

Application of the Shuffled Frog Leaping Algorithm for the Optimization of a General Large-Scale Water Supply System

A water supply system is a complex network of pipes, canals and storage and treatment facilities that collects, treats, stores, and distributes water to consumers. Increasing population and its associated demands requires systems to be expanded and adapted over time to provide a sustainable water supply. Comprehensive design tools are needed to assist managers determine how to plan for future growth. In this study, a general large-scale water supply system model was developed to minimize the total system cost by integrating a mathematical supply system representation and applying an improved shuffled frog leaping algorithm optimization scheme (SFLA). The developed model was applied to two hypothetical water communities. The operational strategies and the capacities for the system components including water transport and treatment facilities are model decision variables. An explicit representation of energy consumption cost for the transporting water in the model assists in determining the efficacy of satellite wastewater treatment facilities. Although the water supply systems studied contained highly nonlinear terms in the formulation as well as several hundred decisions variables, the stochastic search algorithm, SFLA, successfully found solutions that satisfied all the constraints for the studied networks.     

Parameters Optimization using Fuzzy Rule Based Multi-Objective Genetic Algorithm for an Event Based Rainfall-Runoff Model

The calibration of an event based rainfall-runoff model for steam flow forecasting is challenging because, it is difficult to measure the parameters physically on the field for each rainfall event. In the present study, Fuzzy rule based Multi-objective Genetic Algorithm (MGA) is developed to optimize the infiltration and roughness parameters of an event based rainfall-runoff model. Nash Sutcliffe Efficiency (NSE), Coefficient of Determination (R²) and transformed volume difference (f(V)) are used as the objective functions of the MGA and all Pareto optimal solutions are identified using Nondominated Sorting method. As three objective functions are included in the calibration, the number of Pareto optimal solutions are also increases and hence, the optimization problem now becomes a decision making problem. Therefore, to select the best solution from all Pareto optimal solutions, a Fuzzy Rule-Based Model (FRBM) is developed to get alternative values of each Pareto optimal solution. First, the Fuzzy rule based MGA is developed by integrating the FRBM with the MGA. Then the Fuzzy rule based MGA is integrated with an event based runoff model. The developed Fuzzy-MGA based runoff model is tested on three different watersheds and the simulation results of Fuzzy-MGA based runoff model are compared with observed data and previous study results. From the simulated events of three watersheds using Fuzzy-MGA based runoff model, it is observed that the mean percentage error in any criteria (i.e. volume of runoff, peak runoff, and time to peak) of the developed model for a watershed is less than 16.33%. It is also noted that the developed Fuzzy-MGA based runoff model is able to produce hydrographs that are much closer to the measured hydrographs.     

基于遗传模拟退火算法的新安江模型参数优选

结合新安江模型参数的特点,在分析传统遗传算法和模拟退火算法各自优缺点的基础上,将模拟退火算法与传统遗传算法相结合的混合算法运用于新安江模型参数优选中,并应用于实际径流预报.实例表明,该方法能快速地完成参数寻优,并找出较为满意的参数最优解.     

飞蛾火焰优化算法在承压含水层参数反演中的应用

通过10个典型低维函数对一种新型群体智能仿生算法——飞蛾火焰优化(MFO)算法进行仿真验证,并与粒子群优化(PSO)算法的寻优结果进行对比。以无界井流问题及直线隔水边界附近井流问题的解析解为基础,将MFO算法应用于分析抽水试验数据,进行反演承压含水层参数,并以2个实例对MFO算法进行验证。结果表明:MFO算法在低维函数极值寻优问题上具有较好的收敛精度和全局寻优能力,寻优精度较PSO算法提高了7个数量级以上。MFO算法对2个实例的反演精度较文献改进SA算法等提高了56.5%以上,具有较好的稳健性能、收敛速度和全局寻优能力。利用MFO算法对承压含水层参数进行反演,可获得比相关文献更高的反演精度,不但为精确估计承压含水层参数提供了有效方法,而且拓展了MFO算法在地下水模型参数反演中的应用,具有良好的应用价值和前景。     

灌区多目标种植结构优化模型研究

建立了以扶余灌区经济效益、节水效益和生态效益为目标的作物种植结构优化模型,提出了灌区种植结构的不同调整方案。模型建立后,采用模糊折中规划方法将多目标函数转化成单目标函数,运用MATLAB软件进行求解。结果表明,该灌区在优先考虑灌溉成本兼顾其他目标时,旱田与水田的种植面积比例为1.53∶1,旱田全部种植玉米;优先考虑节水效益兼顾其他目标时,灌区全部种植玉米;优先考虑生态效益兼顾其他目标时,旱田与水田的种植面积比例为1.48∶1,玉米与大豆的种植面积比例为0.99∶1。     

A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters

Water Resources Management - In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for the assessment of...