基于改进混合蛙跳算法的梯级水库优化调度

针对混合蛙跳算法在寻优过程中出现的早熟收敛问题,利用混沌技术的遍历性优势对子群最优个体进行变异操作,形成局部精细搜索策略;根据蛙群相对多样性参数来判断算法是否陷入局部最优,进而对蛙群最优个体进行扰动以提高全局寻优能力,形成全局激励调节策略。耦合2种策略,提出了一种改进混合蛙跳算法。将其应用于李仙江梯级水库优化调度中,结果表明所提算法具有寻优质量高、收敛速度快的特点,有效地克服了标准混合蛙跳算法的早熟缺陷,为水库调度模型的求解提供了一种新方法。     

The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

Water Resources Management - The short-term economical operation (STEO) in hydropower station is nonlinear mixed integer problem, satisfying complex hydrologic constraints simultaneously. In this...     

动态差分进化算法在梯级水库优化问题中的应用

采用标准差分进化算法在求解梯级水库优化问题时,随着解链长度的增加,算法求解性能下降,进化后期种群多样性降低,算法极易陷入局部最优解。为此,定义了个体差异参数来动态控制差分进化算法的缩放因子,即定义算法可进化度参数来动态控制算法的选择机制。通过对比标准差分进化算法、逐步优化算法和动态差分进化算法求解2个标准测试函数和某梯级水库优化调度的模拟仿真结果,发现后者较前者的全局搜索能力有了显著提高。     

模拟植物生长算法在水库群优化调度中的应用

介绍了模拟植物生长算法的基本原理,针对梯级水电站优化调度问题的特点,建立了该问题的模拟植物生长算法求解模型.对基本模拟植物生长算法进行了改进,并设计了相应的算法步骤.在此基础上,将基本模拟植物生长算法及其改进算法应用于水库群优化调度中.通过对雅砻江梯级优化调度的应用分析,表明由形态素浓度决定其方向性和随机性搜索机制的模拟植物生长算法具有较强的全局搜索能力,为解决水库群优化调度问题提供了新的途径.     

Multi-Strategy Improved Slime Mould Algorithm and its Application in Optimal Operation of Cascade Reservoirs

Water Resources Management - Slime mould algorithm (SMA) is extensively used in engineering applications such as the parameter estimation of photovoltaic models, image segmentation, economic...     

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.     

Random Bagging Classifier and Shuffled Frog Leaping Based Optimal Sensor Placement for Leakage Detection in WDS

Water Resources Management - Water Distribution Systems (WDS) are the large scale systems that demand design of enhanced leak detection and isolation techniques to prevent from water waste....     

梯级水库群联合优化调度算法研究综述

系统介绍了梯级水库群联合调度模型求解的常规优化方法和智能优化方法,其中常规优化方法包括动态规划法及其改进方法和大系统分解协调等方法,智能优化方法包括粒子群优化算法、遗传算法、差分进化算法等。在评述各种方法的基础上,给出了大规模水库群联合优化调度需要解决的问题:1模型系统求解的稳定性和求解效率的平衡问题;2考虑生态环境的多目标优化调度模式;3水库群优化调度模型描述方法;4优化模型输入和输出的不确定性问题;5机组高水头多振动区运行问题。同时,指出了智能优化算法的发展方向:1数学理论基础研究;2参数对算法结果的影响分析;3各种优化算法的改进与融合;4求解算法的并行计算和云计算技术。     

Integration of Shuffled Frog Leaping Algorithm and Support Vector Regression for Prediction of Water Quality Parameters

Quality of surface water is a serious factor affecting human health and ecological systems. Accurate prediction of water quality parameters plays an important role in the management of rivers. Thus, different methods such as (support vector regression) SVR have been employed to predict water quality parameters. This paper applies SVR to predict eight water quality parameters including (sodium (Na⁺), potassium (K⁺), magnesium (Mg⁺²), sulfates (SO₄ ^?2), chloride (Cl^?), power of hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS)) at the Astane station in Sefidrood River, Iran. To achieve an efficient SVR model, the SVR parameters should be selected carefully. Commonly, various techniques such as trial and error, grid search and metaheuristic algorithms have been applied to estimate these parameters. This study presents a novel tool for estimation of quality parameters by coupling SVR and shuffled frog leaping algorithm (SFLA) . Results of SFLA-SVR compared with genetic programming (GP) as a capable method in water quality prediction. Using SFLA-SVR, average of RMSE for training and testing of six combinations of data sets for all of the water quality parameters improved 57.4 % relative to GP. These results indicate that the new proposed SFLA-SVR tool is more efficient and powerful than GP for determining water quality parameters.     

Adaptive Genetic Algorithm for Daily Optimal Operation of Cascade Reservoirs and its Improvement Strategy

For the specialty of cascade reservoirs optimization and the premature convergence of GA, several improvement strategies are presented in this paper. Firstly, solution space generation method is found application to generate feasible initial population. Secondly, chaos optimization is adopted to optimize initial population. Thirdly, new selective operators, trigonometric selective operators, are proposed to overcome the fitness requirement of non-negative and to maintain the diversity of population. Fourthly, adaptive probabilities of crossing and mutation are adopted in order to improve the convergence speed of GA. Besides, elitist strategy is used to ensure that the best individual can be remained in each generation. Furthermore, the performance of these proposed improvement strategies was checked against the historical improvement strategies by simulating optimal operation of Three Gorges cascade reservoirs premised on historical hourly inflows, and the comparison yields indications of superior performance. In these proposed improvement strategies, trigonometric selective operators are feasible and effective for optimizing operation of cascade reservoirs. These new selective operators could help GA to find a more excellent solution in the same algebra, and the performance of convergence speed is advanced. Adaptive probabilities of crossing and mutation have better performance than other improvement strategies, such as annealing chaotic mutation and simulated annealing of large probability of mutation, because this method realizes the twin goals of maintaining diversity in the population and advancing the convergence speed of GA.     

多目标免疫遗传算法在梯级水库优化调度中的应用研究

为了提高遗传算法在多目标梯级水库优化调度中的应用效果,在标准遗传算法的基础上引入了免疫机制,并将其应用到周公宅-皎口水梯级水库优化调度中。计算结果表明,免疫遗传算法较好地克服了标准遗传算法收敛速度慢、易陷入局部极值、早熟等弱点。     

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。     

基于状态转移矩阵的梯级水库优化调度确定性离散动态规划方法

基于状态转移矩阵的确定性离散动态规划(DDDP)方法是在考虑水库运行的综合利用的前提下,利用状态转移矩阵寻找出梯级水库优化调度过程中所有的可行策略,基于目标函数逆向搜索可行策略,得出最优解的优化算法.该方法可进行单库优化计算,也可以进行梯级水电站优化计算.文中以锦屏梯级为例,说明该方法可在满足梯级水库上下游生态用水、发电用水等综合利用的前提下,以梯级水电站发电效益最大为目标,求得梯级水库优化调度的较好解.     

蜂群遗传算法及在水库群优化调度中的应用

介绍了一种基于蜂群繁殖原理的改进遗传算法--蜂群遗传算法(BSGA),采用改进的遗传算子解决了传统遗传算法中"选择压力"和"种群多样性"之间的矛盾.将该算法应用于梯级水库优化调度的研究中,通过对清江梯级水库群优化调度的实例计算,得到了合理的全局最优解,验证了BSGA的可靠性和实用性.     

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。     

梯级水库联合优化调度模型及其应用

为充分挖掘梯级水库间的水文补偿和库容补偿潜力,获取更高的综合利用效益,提出应用判别系数与水库蓄供水控制线相结合的方法,并结合免疫粒子群算法,以梯级电站发电量最大为目标,对水库蓄供水控制线进行优化。以乌江梯级水库为例,对其蓄供水方式进行了研究。联合调度结果表明,优化后的水库蓄供水控制线不仅可以合理控制水库蓄放水次序和蓄放水量,达到合理控制水库群运行方式的目的,而且在实际调度中具有良好的可操作性和抗风险性。     

梯级水库联合优化调度模型及其应用

为充分挖掘梯级水库间的水文补偿和库容补偿潜力,获取更高的综合利用效益,提出应用判别系数与水库蓄供水控制线相结合的方法,并结合免疫粒子群算法,以梯级电站发电量最大为目标,对水库蓄供水控制线进行优化。以乌江梯级水库为例,对其蓄供水方式进行了研究。联合调度结果表明,优化后的水库蓄供水控制线不仅可以合理控制水库蓄放水次序和蓄放水量,达到合理控制水库群运行方式的目的,而且在实际调度中具有良好的可操作性和抗风险性。     

基于改进调度函数的梯级水库群联合调度研究

梯级水库联合调度规律是指导梯级水库优化运行的重要手段,目前尚无完整的梯级水库群调度规律的理论体系.为此,提出一种可获取梯级水库群联合调度规律的改进调度函数方法,并应用逐步回归法对调度函数进行拟合及相关因子的选择.以乌江流域梯级水库群为例,开展其联合调度研究.结果表明,该方法计算结果较原设计方案,多年平均发电量比设计值增加了14.43亿kW.h,涨幅达5％,保证出力提高到2431.9 MW,经济效益显著.     

基于混合粒子群算法的梯级水电站多目标优化调度

提出多目标混合粒子群算法以解决梯级水电站多目标联合优化调度模型求解的难题。该算法采用混合蛙跳算法的分组－混合循化优化框架,增强算法全局搜索能力,在族群内通过粒子群算法高效灵活的飞行调整策略指导个体进化,同时,引入外部精英集,建立一种基于自适应小生境的外部精英集维护策略,提高算法的收敛性和非劣解集的多样性。最后将该算法应用于三峡梯级水电站多目标优化调度工程应用实例,结果表明,本文算法能够获得计算实时性强、分布均匀、收敛性好的调度方案集,并以此分析明确了调度目标间的耦合关系,为梯级电站的多目标调度决策提供了科学依据。     

粗粒度并行自适应混合粒子群算法及其在梯级水库群优化调度中的应用

为了充分利用现今普及的多核配置计算机,提高大规模梯级水库群优化调度问题的求解效率,提出了梯级水库群优化调度的粗粒度并行自适应混合粒子群算法。该方法以自适应混合粒子群算法为求解基础,采用粗粒度并行设计模式,利用Fork/Join多核并行框架的分治策略,将其初始种群递归划分为多个子种群,平均分配到不同的内核逻辑线程中实现并行计算,并在各子种群优化结束后,合并优化结果集从而输出全局最优解。以澜沧江下游梯级水库群发电优化调度为例,利用该方法进行计算。结果表明,该方法能充分发挥多核配置的计算性能,在4核环境下最大加速比达到3.97,缩短计算耗时1 787.2 s,计算效率显著提高,为我国不断扩张的大规模梯级水库群优化调度提供了一种切实可行的高效求解途径。