Layout and Component Size Optimization of Sewer Network Using Spanning Tree and Modified PSO Algorithm

In the paper, a new method is introduced for optimally solve the problem of the layout and component size determination of sewer network. Simultaneously Layout and component size optimization of sewer network problem consists of many hydraulic constraints which are generally nonlinear and discrete; which creates a challenge even to the modern heuristic search methods. An algorithm generation of a predefined number of spanning trees is introduced to generate a predefined number of sewer layouts of a base sewer network in order of increasing length. These generated layouts are sorted in ascending order of total cumulative flow and sorted layouts are individually optimized for sewer components sizing. It has been found that the optimal sewer layout for total system optimization is one where the total cumulative flow has the minimal value. The modified particle swarm optimization (MPSO) algorithm has been used to optimally determine the component sizes of the selected layouts. The proposed method is applied to the Sudarshanpura sewer network (situated in Jaipur, India) design problem. The results are presented for optimal cost vs cumulative flow of the layouts. Further results of MPSO has been compared with the original PSO algorithm.     

基于自适应遗传算法的配电网改造方案优化

针对当前中国配电网需要在投资有限的情况下从多个角度进行全方位改造的现状，提出一种基于自适应遗传算法的配电网改造方案优化方法。针对当前电网的薄弱环节制定了多个具有针对性的改造项目;设定改造项目之间依赖、互斥关系并设计了相应的编码方式和交叉、变异算子;在最大投资约束下，采用自适应遗传算法对全部项目进行取舍，得出综合评价指标最高的项目组合作为优化结果。算例表明，该方法在现状网缺陷较多、改造投资不足的情况下能够取得满意的结果。     

Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm

Water Resources Management - Short-term hydropower generation scheduling (STHGS), a highly complicated nonlinear optimization problem with various equality and inequality constraints, plays an...     

基于改进遗传算法的生态友好型水库调度

以锦屏梯级水库为案例,从系统工程论的角度出发,将梯级水库作为物理系统,以年发电量为目标函数,建立了梯级水库调度优化模型。为减少水库调度对河道生态系统的影响,在鱼类栖息地模拟的研究基础上,引入目标物种的生态需水过程对调度模型进行动态约束,并采用改进的遗传算法进行求解。研究得到了满足目标鱼类生态需水条件下发电量最大的梯级水库调度策略,并对生态流量满足程度与工程效益损失之间的定量响应关系进行了研究,提出了折中方案选择的基本原则。     

Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies.     

用遗传算法求解给水管网系统优化改扩建模型

针对给水管网系统所面临的改扩建问题 ,建立了给水管网系统实用优化改扩建模型。采用遗传算法求解 ,一次性解决了求解管径标准化的难题 ,同时计算得到水源最优分配流量。在不影响水力模型计算精度的前提下 ,引入管网简化 ,对非改扩建区域的管网实现任意程度的简化 ,大大提高了计算速度 ,并用实例进行了验证     

Optimization of Multireservoir Systems by Genetic Algorithm

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water resources planning and management. As an optimization Genetic Algorithm, ruled by evolution techniques, have become popular in diversified fields of science. The main aim of this study is to explore the efficiency and effectiveness of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results obtained were compared to the real operational data and genetic algorithm was found to be effective and can be utilized as an alternative technique to other traditional optimization techniques.     

A GA-HP Model for the Optimal Design of Sewer Networks

This paper illustrates the application of a new model combined Genetic Algorithm with Heuristic Programming (GA-HP) technique in order to establish the optimal design for sewer networks. The objective is to minimise the construction cost function, which is represented by the depth of excavation and pipe diameter. The proposed GA-HP model has achieved the optimum design task in two stages. Firstly, the Genetic Algorithm (GA) was applied to obtain the diameters of the pipes needed for the preliminary design of the network. Secondly, Heuristic Programming (HP) preliminary designs were used to obtain the optimal slope for those pipes and to determine other characteristics such as the velocity, relative depth of water, excavation depths and total cost of the network. A MATLAB code was used to perform the GA-HP optimisation modelling. The performance of three different selection methods, four different crossover methods and different population sizes is examined with the proposed model, to determine their impact on convergence behaviour. The proposed GA-HP model is tested using some benchmark examples of sewer networks from the literature. The results show that the GA-HP model is superior to all previous methods and may be more efficient in the design of large networks.     

基于遗传算法的TOPMODEL参数优化

模型参数的确定是模型研制与应用成功与否的关键．一般采用人工经验率定和自动率定两种方法来确定．遗传算法和一般的优化算法不同,它具有全局寻优能力,是一类优秀的非线性函数优化算法．利用遗传算法来进行TOPMODEL参数优化,并和人工率定的计算结果作了简要的对比,明显地显示该方法的优点．采用遗传算法所得到的精度较高,可以类推到其他水文模型的参数优化中去．     

Single Reservoir Operating Policies Using Genetic Algorithm

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.     

Multiobjective Genetic Optimization Approach to Identify Pipe Segment Replacements and Inline Storages to Reduce Sanitary Sewer Overflows

Sanitary sewer overflows (SSOs) is the unintentional discharge of untreated sewage from the sanitary sewer system and pose serious risk to public health and to the environment. Rehabilitation plans to reduce SSOs involve increasing conveyance capacity and shaving peak flow using detention storages. Identifying the best location for rehabilitating the sanitary sewer network is a difficult task because of the great length of sanitary sewer systems. This study utilized single and multiobjective genetic algorithms (GAs) to design rehabilitation strategies for SSOs reduction in an existing sewer network. The Nondominated Sorting Genetic Algorithm II was linked to the EPA-SWMM to generate non-dominated sets of solutions that characterizes the tradeoffs between reduction in number of SSOs and cost (Case I), and the tradeoff between of volume of SSOs and cost (Case II). The results show that, when maximizing the reduction of number SSOs, the algorithm target first regions of the network with higher density of SSOs. When maximizing the reduction of volume of SSOs, the solutions prioritize the nodes with the largest overflow volumes. The tested approach provides a range of options to decision makers that seek to reduce or eliminate SSOs in an existing sanitary sewer system.     

一种模糊变步长自适应谐波检测算法

电力有源滤波器的成功应用依赖于精确的谐波电流检测技术。基于自适应干扰对消理论，提出一种基于模糊变步长推理的最小均方差（LMS）自适应谐波检测算法。通过分析影响LMS自适应谐波算法性能的不利因素，选取均方误差变化量和输入输出信号相关函数作为参量，建立模糊推理系统，自适应地调节算法的步长，实现谐波检测过程中，既能保证较快的动态响应速度和对噪声干扰的抑制，又能保持较高的检测精度，并通过计算机仿真及物理实验验证了该算法的有效性和可行性。     

Sewer System Design Using Simulated Annealing in Excel

An optimization procedure has been developed for branching storm and sanitary sewer systems with a pre-determined layout for determining the minimum total cost. The model was developed within Microsoft Excel using simulated annealing as the optimization procedure. The total cost of the storm sewer system that was obtained with this optimal design procedure was compared to the total cost of the system as obtained from the conventional straight slope design procedure. Applying the simulated annealing optimizer to the design of the branching storm sewer network resulted in a cost savings of over $77,100 or about 7 % (a reduction from $1,117,700 to $1,040,600). These significant savings were realized by simply going an extra step and implementing an optimization technique during the design phase. Use of Excel should enhance the availability and the usage of such an optimization model for the design of storm and sanitary sewer systems by consulting engineers and various agencies.     

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.     

基于自适应遗传算法的重力坝体型优化设计

针对简单遗传算法中交叉概率与变异概率随进化过程恒定不变的缺点,提出了自适应遗传算法并将其用于重力坝体型优化设计。实例计算并与复行法优化方法相比,该算法不仅能得到更好的优化结果,而且保持了较快的收敛速度。     

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。     

Utility-Based Maintenance Optimization for Complex Water-Distribution Systems Using Bayesian Networks

Water supply systems (WSS), as well as other real-world systems, are characterized by complex configurations. For these systems, it is essential to ensure appropriate utility through optimal maintenance planning. The difficulties in decision-making are much increased by lack of information regarding the operation and failure conditions. When maintenance optimization is considered for systems configured as networks, comprising a large number of components, the main challenge is to model the reliability characteristics, such as availability, taking account of the interactions and dependencies between different components. The aim of this paper is to provide an optimal Preventive Maintenance (PM) plan with a view to maximizing the utility of a complex repairable system using Bayesian Networks (BNs). For each node of the BN, the optimal PM periodicity is obtained, in accordance with the policy of periodic imperfect PM with minimal repair at failure. The system availability is then computed, by Bayesian inference, for various combinations of nodes, or subsystems, periodicities and partial renewals before the complete renewal of the whole system. A utility function is then introduced to provide the maintenance plan for the system, leading to the implementation of the best policy. The methodology is illustrated by numerical application on WSS.     

基于高阶累积量Burg自适应算法的间谐波估计

Burg算法适合处理间谐波信号，但是在有噪声的环境下，会产生错误的谱估计结果。采用一种基于高阶累积量的Burg自适应算法估计间谐波信号，该方法不受高斯噪声的影响。首先，利用高阶累积量对高斯噪声不敏感的特性，改进Burg算法的预测误差标准。其次，为了降低引入高阶累积量而增加的计算量，反射系数的求取采用递推形式。最后，利用谐波信号的4阶累积量对角切片求取各成分的幅值。仿真结果表明，所提出的算法明显改进了Burg算法的谱估计性能，能够在噪声环境下获得准确的间谐波参数估计。     

基于加速遗传算法的堆石坝施工优化

为实现水电站施工资源优化配置,提高水电站建设的经济效益,提出了堆石坝施工优化模型;模型中考虑了日上升高程约束和相邻区的高差约束,并用加速遗传算法实现了优化计算。对国内某一在建堆石坝的计算结果表明．该方法是可行的,对大坝建设期间的资源配置和强度计划制定有一定指导意义。     

Long-Term Stochastic Reservoir Operation Using a Noisy Genetic Algorithm

To deal with stochastic characteristics of inflow in reservoir operation, a noisy genetic algorithm (NGA), based on simple genetic algorithms (GAs), is proposed. Using operation of a single reservoir as an example, the results of NGA and Monte Carlo method which is another way to optimize stochastic reservoir operation were compared. It was found that the noisy GA was a better alternative than Monte Carlo method for stochastic reservoir operation.