MOVPSO算法在TOPMODEL参数优化中的应用

为了对水文模型中难以直接测算的参数进行调试和优化,将多目标涡流粒子种群优化算法(Multi-Objective Vortex Particle Swarm Optimization,MOVPSO)应用于水文模型参数优化计算中,并以逼近性(Generational Distance,GD)及超体积值(Hyper-Volume,HV)作为算法性能评价指标。将MOVPSO算法与NSGA-Ⅱ算法及多目标粒子种群优化算法(Multi-Objective Particle Swarm Optimization,MOPSO)独立运行50次所得Pareto前沿的GD值及HV值进行统计分析,并结合方差分析比较3种算法的性能。将MOVPSO算法迭代过程中的粒子种群速率、种群半径的估计值与测量值进行对比分析并判别两者的拟合程度。用尼泊尔巴格玛蒂河流域2005~2011年期间实测洪水日径流过程资料作为TOPMODEL模型参数率定系列,运用MOVPSO算法对模型参数进行优化,得出Pareto最优解,并利用2013年5场洪水日径流过程进行模型检验。结果表明:MOVPSO算法所得Pareto解集性能优于NSGA-Ⅱ及MOPSO算法,拟合历史洪水平均确定性系数达到0.85,模型预报精度高,表明MOVPSO优化算法在解决多参数多目标优化问题中具有优势。     

PA-DDS算法在水库多目标优化调度中的应用

Pareto存档动态维度搜索（Pareto-Archived Dynamically Dimensioned Search,PA-DDS）算法是一种求解多目标问题的随机搜索启发式算法。本文将PA-DDS算法引入考虑供水和发电的多目标优化模型优化水库调度图,与非支配排序遗传算法（NSGA-II）和多目标粒子群算法（MOPSO）对比了收敛性,并在求得非劣解分布的均匀性和与理论Pareto前端的相似性方面与NSGA-II进行比较,分析该算法求解水库多目标调度问题的优化性能,对比分析不同目标下的优化调度图。结果表明：PA-DDS算法能够得到更高质量的非劣解集,优化调度图与原设计调度图相比,能更有效协调供水和发电的矛盾,在小幅降低（0.96%）发电量的前提下显著提高（8.07%）水库供水量,平均每年增加经济效益0.55亿元。     

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。     

PA-DDS算法在水库多目标优化调度中的应用

Pareto存档动态维度搜索(Pareto-Archived Dynamically Dimensioned Search,PA-DDS)算法是一种求解多目标问题的随机搜索启发式算法。本文将PA-DDS算法引入考虑供水和发电的多目标优化模型优化水库调度图,与非支配排序遗传算法(Non-dominaled Sorting Genetic AlgorithmⅡ,NSGA-II)和多目标粒子群算法(Multi-Objectives Particlce Swarm Optimization,MOPSO)对比了收敛性,并在求得非劣解分布的均匀性和与理论Pareto前端的相似性方面与NSGA-II进行比较,分析该算法求解水库多目标调度问题的优化性能,对比分析不同目标下的优化调度图。结果表明:PA-DDS算法能够得到更高质量的非劣解集,优化调度图与原设计调度图相比,能更有效协调供水和发电的矛盾,在小幅降低(0.96%)发电量的前提下显著提高(8.07%)水库供水量,平均每年增加经济效益0.55亿元。     

PA-DDS算法在新安江模型参数优化中的应用

为提高新安江模型参数的自动率定效率,首次利用多目标优化方法将PA-DDS算法用于模型日模和次洪参数优化中,并与NSGA-Ⅱ和AMALGAM算法进行对比。结果显示:PA-DDS算法比另外2种算法能够更快地得到Pareto最优解且解的质量较好,次洪平均确定性系数达到近0.84,部分确定性系数达到0.97,模型预报精度高,表明PA-DDS算法在多目标多参数优化中效果较好,在新安江模型中具有明显优势。     

基于MOPSO的RC单柱墩全寿命抗震性能多目标优化设计

采用MATLAB模块化编程将多目标粒子群优化算法MOPSO和反应谱分析联合起来进行抗震优化设计。根据桥墩在E1和E2两级设防水准地震作用下的抗震性能,采用Pushover分析墩顶的最大漂移率以及概率统计的方法得到全寿命期费用模型对应的工程费用,从而得到费用最小的Pareto解。数值计算结果表明:MOPSO算法和反应谱分析结合能够很好的应用于桥梁全寿命抗震性能设计分析,算法的收敛性和稳定性好,所求得的Pareto最优解在解空间分布均匀且范围较广,可以为桥梁设计提供良好的方案选择空间。更多还原     

基于多决策变量协同设计的供水管网多目标优化模型研究

在城镇供水管网设计中,管线布置和管径搭配是影响工程经济性和可靠性的重要因素。为提升管网设计的整体优化水平,以管线布置和管径搭配共同作为决策变量,选用"管网建造费用"作为经济性目标函数,选用"管网管段压力均值"、"管网恢复力"和"枝状管流量和"作为管网可靠性目标函数,建立供水管网多目标优化设计模型。使用MATLAB平台搭载EPANET2动态链接库,采用改进的非支配排序多目标遗传算法(NSGA-Ⅱ)求解模型得到帕累托(Pareto)前沿解集,并使用多标准优化和妥协解决方案(VIKOR)法选出最优折衷设计方案。最后以广东省某城镇供水管网设计为例进行验证,得到的前沿解集在多维目标空间中分布均匀,且选出的最优折衷方案综合效益良好,证明了供水管网管线布置和管道尺寸协同设计的科学性和有效性。     

基于改进NSGA-Ⅱ算法的水资源多目标优化配置

山西省作为资源性缺水大省,对水资源进行优化配置是缓解用水紧张和提高社会经济发展水平的有效手段。通过建立山西省"大水网"晋中-长治供水区水资源多目标优化配置模型,对NSGA-Ⅱ算法在约束条件处理和遗传操作越界处理方面进行改进,并用于模型的求解,得出Pareto最优解集,最后从不同的侧重点挑选出3个不同的优化配置方案,以供决策者根据实际需要进行选择。综合来看,改进NSGA-Ⅱ算法较传统NSGA-Ⅱ算法更有效可行,配置结果合理。     

基于改进多目标粒子群算法的平原坡水区水资源优化调度EI北大核心CSCD

在分析平原坡水区水资源优化调度特点的基础上,构建了协调泵站提水量和受水区缺水量两个目标的优化调度模型。针对传统粒子群算法存在的容易陷入局部极值点、早熟等缺点,从惯性因子及学习因子选择、外部档案维护和全局最优选取策略3个方面进行改进,对比改进多目标粒子群算法与传统NSGA-Ⅱ算法在求解测试函数中的表现,验证改进算法的可行性和优越性。对宿迁市黄河故道及以南地区水资源优化调度进行实例研究,采用改进算法求解模型得到Pareto前沿,结果显示,两个目标函数值分布范围较广,且各频率来水调度方案集在空间中分布均匀。     

大型复杂跨流域调水工程电站-水库-泵站群多目标优化调度

考虑调水量、发电最大和耗能最小3个目标,建立电站-水库-泵站群多目标优化调度模型。基于参数调整策略、邻域变异和加速策略,提出了求解多目标优化调度模型的综合改进布谷鸟新算法并求解模型,获得了发电、调水、泵站耗能多目标Pareto解集。以引汉济渭大型复杂跨流域调水工程为实例,将该模型与模拟调度模型和NSGA-Ⅱ算法多目标优化调度模型的结果进行比较,结果表明,该优化调度模型的发电、调水、耗能、弃水等各项指标合理,具有相对优势。     

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.     

Modeling and Combined Application of Orthogonal Chaotic NSGA-II and Improved TOPSIS to Optimize a Conceptual Hydrological Model

Conceptual rainfall-runoff modelling is a widely-used approach for rainfall-runoff simulation in streamflow forecasting. The objective of this paper is to introduce an improved non-dominated sorting genetic algorithm-II (NSGA-II) for multi-objective automatic calibration of a hydrologic model. The orthogonal design based initialization technique is exploited to produce a more uniformly-distributed initial population. At the same time, a chaotic crossover operator as well as a chaotic mutation operator are presented to avoid trapping into local minima and to obtain high quality solutions. Finally, a multi-criteria decision-making (MCDM) approach combing Shannon entropy weighting method and an improved technique for order preference by similarity to ideal solution (ITOPSIS) based on projection is introduced to prioritize the Pareto optimal solutions and select the comprehensive optimal solution as a follow-up step. Hydrological data from two river basins named the Leaf and Muma River basins are exploited to test the ability of the orthogonal chaotic NSGA-II (OCNSGA-II) for solving the multi-objective HYMOD (MO-HYMOD) problem. The results demonstrate that the OCNSGA-II can obtain better-distributed Pareto optimal front and thus can be exploited as an effective alternative approach for the multi-objective automatic calibration of hydrologic model.     

Water Quality Sensor Placement: A Multi-Objective and Multi-Criteria Approach

To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.

     

Multi-Objective Optimization of the Proposed Multi-Reservoir Operating Policy Using Improved NSPSO

Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy.     

Multi-objective Cultured Differential Evolution for Generating Optimal Trade-offs in Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a complex problem because it needs to consider multiple objectives and a large number of constraints. Traditional methods usually convert multiple objectives into a single objective to solve, using weighted methods or constrained methods. In this paper, a new approach named multi-objective cultured differential evolution (MOCDE) is proposed to deal with RFCO. MOCDE takes cultural algorithm as its framework and adopts differential evolution (DE) in its population space. Considering the features of DE and multi-objective optimization, three knowledge structures are defined in belief space to improve the searching efficiency of MOCDE. MOCDE is first tested on several benchmark problems and compared with some well known multi-objective optimization algorithms. On achieving satisfactory performance for test problems, MOCDE is applied to a case study of RFCO. It is found that MOCDE provides decision makers many alternative non-dominated schemes with uniform coverage and convergence to true Pareto optimal solutions in a short time. The results obtained show that MOCDE can be a viable alternative for generating optimal trade-offs in reservoir multi-objective flood control operation.     

Operating Rules of an Irrigation Purposes Reservoir Using Multi-Objective Optimization

A multi-objective optimization technique for the operation of an irrigation reservoir is presented in this paper. The study deals with two different objective functions (OF): the minimization of reservoir release deficit from the irrigation demand (OF1) and the maximization of net benefit by the demand sector (OF2). In the first step, monthly optimization of each individual objective was performed with a deterministic non-linear programming (NLP) algorithm, that gave the lower and upper bounds for the multi-objective analysis. In the second step, multi-objective optimization was performed through the Constraint method that operates by optimising the objective function OF1, while the other (OF2) was constrained to satisfy release strategies generated by the optimization. Non-dominated set of release strategies is generated by parametrically varying the bounds of the constraints obtained from the individual optimal solutions. In the third step, the interactive analytical Step method was applied to find the best compromise solution, between the two OFs, by minimizing the distance of each non-dominated solution to an ideal solution that represents the utopian optimum for both OF1 and OF2. Furthermore, the interactive approach allows to improve the performance of the reservoir in terms of compromise irrigation releases, by changing the OF values until the satisfaction of predetermined criteria fixed by the planners and decision makers. The proposed water allocation model was applied to the Pozzillo reservoir operation, that supplies the Catania Plain irrigation area (Eastern Sicily).     

基于NSGA-Ⅱ算法最优组合的风电功率预测的研究

风电场的出力是一个受风速波动性和各种气象条件影响的复杂过程,风电功率预测的准确性可以大大提高电力系统调度运行的效率,维持发、输、用电之间功率的平衡。针对于此,对风电场进行功率预测时,建立了表征风电功率波动的平稳性指标,考虑到风电的波动性越小,预测精度就越高,引入了带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ),以此来求取各个风力发电机组的最优组合,使得组合后的风电出力更加平稳,波动更小,得到了一组pareto最优解集。然后对pareto解集中的所有组合的风力发电机组,利用BP神经网络进行功率预测,预测精度最高的解就是最优的组合。通过仿真验证,证明该方法的有效性和合理性。并将所得到的结果与经典的风电功率预测方法—小波预测和粒子群优化的BP神经网络(PSOBP)预测进行对比分析,证明了所提方法的优越性。     

基于多目标差分进化算法的水库多目标防洪调度研究

为求解水库多目标防洪优化调度问题,提出一种基于自适应柯西变异的多目标差分进化算法,克服了早熟收敛问题,提高了收敛精度;同时,根据多目标优化的特点对差分算子进行修正,并引入外部档案技术,提高了算法的收敛速度。本文以坝前最高水位最低、最大下泄流量最小和汛末水位最接近汛限水位为目标,对三峡水库展开多目标防洪调度研究,结果表明,该算法可在较短时间内生成大量在各目标分布均匀、分布范围广的非劣调度方案供决策者评价优选,为水库多目标防洪调度决策提供了一种新的调度方案生成方法。