Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.     

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。     

基于MATLAB环境的抗滑桩智能优化设计系统

介绍了基于MATLAB的抗滑桩智能优化设计系统的基 本思路,并给出了该系统的总体结构、主要功能以及主要模块--抗滑桩BP网络结合模拟退火遗传算法SAGA(Simulated Annealing Genetic Algorithm)优化设计模块。该系统主要实 现了非线性优化方法和BP网络结合模拟退火遗传算法两种优化方法智能优化抗滑桩设计,最后将该系统应用于云南省祥临公路古滑坡防治方案优化设计。     

基于单纯形-模拟退火算法的多阻尼控制器协调设计

提出一种基于单纯形-模拟退火算法的电力系统稳定器(PSS)参数优化方法。该方法以弱阻尼振荡模式构建目标函数,将单纯形法搜寻机理嵌入到模拟退火算法的基本步骤中,综合了模拟退火算法全局搜索能力强及单纯形算法局部收敛速度快的优点。四机典型系统上的特征根分析表明,该方法是一种有效的阻尼控制器优化方法,所得的参数对系统运行方式的变化具有良好的鲁棒性。     

基于MPI环境的并行模拟退火算法及其工程应用

介绍了基于MPI的并行编程环境;从传统的串行模拟退火算法出发,提出了并行模拟退火算法的并行思路和具体实现,给出了模拟退火算法并行实现的关键-并行随机数产生方法。最后通过一个拱坝优化设计的工程实例,说明并行模拟退火算法的正确性和高效性。     

模拟退火算法的改进及其在边坡稳定分析中的应用

首先对模拟退火算法进行改进,将遗传算法的群体、交叉、变异等概念引入其中,使得它能从多个初始点开始并行寻优,能以较快的速度找到全局最优解。然后基于有限元应力场,应用改进的模拟退火算法建立边坡任意形状最危险滑裂面及相应最小安全系数的全局优化搜索方法。通过典型算例分析,证明应用改进的模拟退火算法搜索边坡最危险滑动面是可行和高效的。     

A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization

Practice experience suggests that the traditional calibration of hydrological models with single objective cannot properly measure all of the behaviors of the hydrological system. To circumvent this problem, in recent years, a lot of studies have looked into calibration of hydrological models with multi-objective. In this paper, we propose a novel multi-objective evolution algorithm entitled multi-objective shuffled complex differential evolution (MOSCDE) algorithm, which is an extension of the famous single objective algorithm, shuffled complex evolution (SCE-UA) algorithm, to the multi-objective framework. This new proposed algorithm replaces the simplex search used in SCE-UA with the differential evolution (DE) algorithm and can more thoroughly utilize the information of the individuals in the evolutionary population and improve the search ability of the algorithm. Meanwhile, the Cauchy mutation (CM) operator is employed to prevent the algorithm from falling into the local optimal region of the feasible space. Moreover, two types of archive sets are employed to further improve the performance of the algorithm. The efficacy of the MOSCDE algorithm is first tested on five benchmark problems. After achieving satisfactory performance on the test problems, the MOSCDE is applied to multi-objective parameter optimization of a hydrological model for daily runoff forecasting. The results show that the MOSCDE algorithm can be a viable alternative for multi-objective parameter optimization of hydrological model.     

基于模拟退火和粒子群混合算法的地应力场分析

针对地应力场分析计算时边界荷载难以确定的问题,将模拟退火和粒子群优化算法引入到地应力场的分析研究中。由于粒子群算法容易陷入局部最优的缺陷,结合模拟退火思想与粒子群优化算法形成一种混合优化算法,以提高粒子群算法的收敛精度。运用该算法对新疆哈德布特水电站地应力场进行分析研究。计算结果表明：地应场计算值和实测值吻合较好,说明该混合优化方法具有较高可靠性。     

Heuristic Optimization Model for the Optimal Layout and Pipe Design of Sewer Systems

The design of urban stormwater systems and sanitary sewer systems consists of solving two problems: generating a layout of the system and the pipe design which includes the crown elevations, slopes and commercial pipe sizes. A heuristic model for determining the optimal (minimum cost) layout and pipe design of a storm sewer network is presented. The hierarchical procedure combines a sewer layout model formulated as a mixed-integer nonlinear programming (MINLP) problem which is solved using the General Algebraic Modeling System (GAMS) and a simulated annealing optimization procedure for the pipe design of a generated layout was developed in Excel. The GAMS and simulated annealing models are interfaced through linkage of Excel and GAMS. The pipe design model is based upon the simulated annealing method to optimize the crown elevations and diameter of pipe segments in a storm sewer network using layouts generated using GAMS. A sample scenario demonstrates that using these methods may allow for significant costs saving while simultaneously reducing the time typically required to design and compare multiple storm sewer networks.     

基于灰色关联度与理想点法的梯级水电站多目标优化调度方法

针对梯级水电站群调度目标间的协调问题,建立了多目标优化调度模型,提出了基于灰色关联度法与熵权理想点法相结合的迭代计算方法。应用灰色关联度法将多目标优化模型转换成多个单目标优化模型,并采用逐步优化算法求解,得到多目标优化数学模型的非劣解集,以熵权理想点法从非劣解集中选择最优解。澜沧江流域梯级水电站群的实例研究表明,该方法较好地处理了不同目标间、不同目标权重组合方案间双重多目标优化问题,为协调长期优化调度多目标间的矛盾提供了一种可行方法。     

大坝及岩基物理力学参数优化反演分析研究

根据大坝原型观测资料识别大坝结构力学参数是典型的非线性反问题,而反问题往往具有Hadamard意义下的不适定性。基于位移反分析方法的基本原理,依据大坝原型观测数据和有限元数值计算成果,建立了优化目标函数后,应用遗传模拟退火算法实现了对大坝及岩基物理力学参数的反演分析。对反演原理、算法及流程进行了研究。将遗传算法和模拟退火算法相结合,既提高了优化问题全局收敛速度,同时克服了传统方法求解病态方程的困难。实例分析表明,该优化反演方法对抵抗噪音有较强的能力,且与正分析形成的闭合环路可以高精度的完成大坝工作性态的分析。     

小生境与遗传模拟退火耦合算法在岩质边坡滑动面搜索中的应用

将小生境思想与遗传模拟退火算法相结合,应用于岩质边坡滑动面的搜索中。在实现了边坡结构面模拟的基础上,小生境与遗传模拟退火耦合算法可以顺利搜索出边坡由结构面与岩桥组合形成的潜在最危险滑动面。与其他智能优化搜索算法相比,该耦合算法具有收敛速度快、可找到搜索函数所有全局最优解、参数依赖性小等优点。工程应用实例表明该耦合算法在岩质边坡滑动面搜索中能取得令人满意的结果。     

Resilience Indexes for Water Distribution Network Design: A Performance Analysis Under Demand Uncertainty

Traditionally, the optimal design of water distrubution networks has been dealt with using single-objective constrained approaches, where the aim is to minimize the network investment cost while maintaining minimum pressure head constraints at all nodes. However, in the last decade some authors have proposed multi-objective approaches which optimize other objectives than network investment cost. In most cases, these objectives have been formulated using the concept of resilience index, which mimics the design aim of providing excess head above the minimum allowable head at the nodes and of designing reliable loops with practicable pipe diameters. Although several authors have proposed different resilience indexes for this pupose, to date there is no empirical study that analyzes the advantages and disadvantages of these proposals. This paper evaluates the performance of a well-known multi-objective evolutionary algorithm, the Strength Pareto Evolutionary Algorithm 2, using three different resilience indexes. The results obtained in two water supply networks under a large number of simulated over-demand scenarios show the advantages and disadvantages of these measures.     

Multi-Objective Firefly Integration with the K-Nearest Neighbor to Reduce Simulation Model Calls to Accelerate the Optimal Operation of Multi-Objective Reservoirs

Reservoirs are used as one of the surface water sources for different and often conflicting water supply purposes. Given the complex management policies governing a basin, it is essential to simultaneously consider different goals and cope with the associated trade-off in water resources management. This purpose requires coupling a multi-objective optimization algorithm with a reservoir simulation model, which this approach increases required computational efforts. Various simulation–optimization approaches have been developed and used for solving the related problems. However, they often have complicated methods and certain limitations in real-world applications. In this study, a new multi-objective firefly algorithm—K nearest neighbor (MOFA-KNN) hybrid algorithm is developed which is time-efficient and is not as complicated as previous approaches. The proposed algorithm was evaluated for both benchmark and real problems. The results of the benchmark problem showed that the execution time of the MOFA-KNN hybrid algorithm was up to 99.98% less than that of the multi-objective firefly algorithm (MOFA). In the real problem, the MOFA-KNN algorithm was linked to the 2D hydrodynamic and water quality model, CE-QUAL-W2, to test the developed framework for reservoir operation. The Aidoghmoush reservoir as a case study investigated to minimize the total released dissolved solids (TDS) and the water temperature difference between the inflow and the outflow. The results demonstrated that the MOFA-KNN algorithm significantly reduced the simulation–optimization execution time (>?660 times compared with MOFA). The minimum released TDS from the reservoir was 13.6 mg /l and the minimum temperature difference was 0.005 °C.

     

改进遗传算法及其在水库群优化调度中的应用

根据梯级水电站优化调度特点,建立遗传算法(GA)求解多阶段最优化问题的数学模型.针对标准遗传算法(sGA)局部寻优能力较差、易早熟等不足之处,从编码方法、遗传算子和混合算法方面对其进行改进,提出了采用超立方体浮点数编码自适应遗传算法(AGA)和超立方体浮点数编码遗传模拟退火算法(SA-GA).通过16种不同策略的GA在雅砻江梯级优化调度中的应用,其结果表明了改进策略在解决水库群优化问题方面的有效性和优越性.最后将GA与动态规划(DP)算法的性能进行比较分析,充分体现了GA的优点.     

引入退火机制的复合形法在边坡最小安全系数搜索中的应用

针对基本复合形法在搜索复杂边坡最小安全系数的过程中可能会陷入局部极小值的问题，在其寻优过程中第一次出现关于最坏点映射失败时，将最坏点作为模拟退火算法的初始寻优点进行一次模拟退火搜索，用寻找到的最优值替换当前复形中的最坏点，构成新的复形继续进行基本复合形法的寻优至结束，从而形成一种新的、更加优异的优化算法。通过对算例的复杂边坡最小安全系数的搜索表明，这种引入退火机制的复合形法是一种全局搜索能力很强的算法。     

Improved Understanding on the Searching Behavior of NSGA-II Operators Using Run-Time Measure Metrics with Application to Water Distribution System Design Problems

In recent years, multi-objective evolutionary algorithms (MOEAs) have been widely used to handle various water resources problems. One challenge within MOEAs’ applications is a lack of understanding on how various operators alter a MOEA’s behavior to achieve its final performance (i.e., MOEAs are black-boxes to practitioners), and hence it is difficult to select the most appropriate operators to ensure the MOEA’s best performance for a given real-world problem. To address this issue, this study proposes the use of the run-time measure metrics to reveal the underlying searching behavior of the MOEA’s operators. The proposed methodology is demonstrated by the non-dominated sorting genetic algorithm II (NSGA-II, a widely used MOEA in water resources) with five commonly used crossover operators applied to six water distribution system design problems. Results show that the simulated binary crossover (SBX) and the simplex crossover (SPX) operators possess great ability in extending the front and finding Pareto-front solutions, respectively, while the naive crossover (NVX) strategy exhibits the overall worst performance in identifying optimal fronts. The obtained understanding on the operators’ searching behavior not only offers guidance for selecting appropriate operators for real-world water resources problems, but also builds fundamental knowledge for developing more advanced MOEAs in future.     

A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System

To address the decision-making problem for real-time multi-objective flood operations in multi-reservoir system, this paper develops a multi-objective best compromise decision model (MoBCDM). Utility function is used to quantitatively express the preference of decision maker, and also fuzzy analytic hierarchy process (FAHP) and segmentation and averaging (Seg/Ave) are adopted together with the preferences of decision participants (hydrologist and reservoir manager) to convert the problem into a scalar optimization. The differential evolution (DE) algorithm is implemented for obtaining the best compromise solution. The multi-objective flood operation problem in Shiguan River Basin (in China), which contains two reservoirs and three flood control points, is used as a case study. The analyses are performed to compare four historical flood operations scenarios, this model and current operating rules. The results of the analyses show that the MoBCDM outperforms all operational scenarios in terms of peak flow reduction at three flood downstream control points. In addition, the MoBCDM execution is very efficient in real-time implementation, and also weighting coefficients for the use by the MoBCDM can get high resolution calculated by FAHP.     

基于文化基因算法的梯级电站负荷分配研究

为了解决传统基于种群进化的搜索算法求解电站负荷分配中搜索精度低、易陷入局部最优的问题,结合文化基因算法的框架,以粒子群算法(PSO)作为全局搜索策略,分别引入爬山算法(HP)与模拟退火算法(SA)作为局部搜索策略,形成HPMA、SPMA两种文化基因算法。设计了相应的局部搜索激活机制,并针对负荷分配问题初始可行解生成效率低的问题提出了一种初始种群快速生成方法。实例计算表明,两种文化基因算法较单独使用SA、PSO等算法具有更好的求解精度,同时SPMA算法优于HPMA算法,SPMA算法有利于提高了梯级水电站负荷分配问题的求解质量。     

A NEW GENETIC SIMULATED ANNEALING ALGORITHM FOR FLOOD ROUTING MODEL

In this paper, a new approach, the Genetic Simulated Annealing (GSA), was proposed for optimizing the parameters in the Muskingum routing model. By integrating the simulated annealing method into the genetic algorithm, the hybrid method could avoid some troubles of traditional meth ods, such as arduous trial and error procedure, premature convergence in genetic algorithm and search blindness in simulated annealing. The principle and implementing procedure of this algorithm were described. Numerical experiments show that the GSA can adjust the optimization population, prevent premature convergence and seek the global optimal result. Applications to the Nanyunhe River and Qingjiang River show that the proposed approach is of higher forecast accuracy and practicability.