共查询到20条相似文献，搜索用时 15 毫秒
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针对概念性水文模型参数众多、相互制约,且多目标参数优化率定最优参数求解困难、易受决策者主观因素影响的问题,采用多目标优化算法对水文模型参数进行率定,得到模型参数最优非劣解集,在此基础上,引入最小最大后悔值决策理论,并结合Pareto支配基本理论,提出了一种多目标最优非劣解选取准则。以柘溪流域为研究对象,采用三目标MOSCDE优化率定新安江模型的参数,并与单目标SCE-UA优化结果进行对比分析。结果表明,提出的非劣解选取方法可以有效从大规模非劣解集中筛选出最优非劣解,大大缩短参数率定耗时。 相似文献
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Optimal design of groundwater remediation systems using a multi-objective fast harmony search algorithm 总被引：1，自引：0，他引：1
A new multi-objective optimization methodology is developed, whereby a multi-objective fast harmony search (MOFHS) is coupled with a groundwater flow and transport model to search for optimal design of groundwater remediation systems under general hydrogeological conditions. The MOFHS incorporates the niche technique into the previously improved fast harmony search and is enhanced by adding the Pareto solution set filter and an elite individual preservation strategy to guarantee uniformity and integrity of the Pareto front of multi-objective optimization problems. Also, the operation library of individual fitness is introduced to improve calculation speed. Moreover, the MOFHS is coupled with the commonly used flow and transport codes MODFLOW and MT3DMS, to search for optimal design of pump-and-treat systems, aiming at minimization of the remediation cost and minimization of the mass remaining in aquifers. Compared with three existing multi-objective optimization methods, including the improved niched Pareto genetic algorithm (INPGA), the non-dominated sorting genetic algorithm II (NSGAII), and the multi-objective harmony search (MOHS), the proposed methodology then demonstrated its applicability and efficiency through a two-dimensional hypothetical test problem and a three-dimensional field problem in Indiana (USA). 相似文献
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提出用Pareto强度进化算法解决供水库群的多目标优化调度问题,算法利用种群的进化过程模拟寻找非劣解集的过程,将供水库群多目标优化调度问题的解当作进化种群中的个体,按照解的Pareto强度值与密度进行适应度计算,利用种群中个体的进化操作获得非劣解,最终整个种群进化为非劣解集。实例分析结果表明,算法能实现多峰搜索,最终非劣解集的分布均匀,且收敛速度快,为解决供水库群多目标优化调度问题提供了一种有效的方法。 相似文献
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This paper presents a new geotechnical design concept, called robust geotechnical design (RGD). The new design methodology seeks to achieve a certain level of design robustness, in addition to meeting safety and cost requirements. Here, a design is considered robust if the variation in the system response is insensitive to the variation of noise factors such as uncertain soil parameters and construction quality. When multiple objectives are considered, a single best design may not exist, and a trade-off may be necessary. In such a case, a genetic algorithm is adopted for multi-objective optimization and a Pareto Front, which describes a trade-off relationship between cost and robustness at a given safety level, is established. The new design methodology is illustrated with an example of spread foundation design. The significance of the RGD methodology is demonstrated. 相似文献
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Multiobjective optimization deals with mathematical optimization problems where two or more objective functions (cost functions) are to be optimized (maximized or minimized) simultaneously. In most cases of interest, the objective functions are in conflict, i.e., there does not exist a decision (design) vector (vector of optimization variables) at which every objective function takes on its optimal value. The solution of a multiobjective problem is commonly defined as a Pareto front, and any decision vector which maps to a point on the Pareto front is said to be Pareto optimal. We present an original derivation of an analytical expression for the steepest descent direction for multiobjective optimization for the case of two objectives. This leads to an algorithm which can be applied to obtain Pareto optimal points or, equivalently, points on the Pareto front when the problem is the minimization of two conflicting objectives. The method is in effect a generalization of the steepest descent algorithm for minimizing a single objective function. The steepest-descent multiobjective optimization algorithm is applied to obtain optimal well controls for two example problems where the two conflicting objectives are the maximization of the life-cycle (long-term) net-present-value (NPV) and the maximization of the short-term NPV. The results strongly suggest the multiobjective steepest-descent (MOSD) algorithm is more efficient than competing multiobjective optimization algorithms. 相似文献
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This paper presents a fuzzy set-based robust geotechnical design (RGD) methodology for the design of shield-driven tunnels. Here, uncertain geotechnical parameters required for analysis of tunnel performance (referred to herein as the structure safety and serviceability performance of tunnel cross section) are represented as fuzzy sets. Given fuzzy input parameters, the performance of a shield-driven tunnel will be uncertain, which is expressed in this study as a fuzzy factor of safety, according to the analysis of vertex method. Then, the fuzzy factor of safety for a given design is used to evaluate the failure probability and design robustness, which are, in turn, employed in the proposed RGD framework. Note that a design is considered robust if the performance of the shield-driven tunnel is insensitive to the variation of its uncertain geotechnical parameters. Within the RGD framework, each candidate design in the design space is analyzed for its safety state (in terms of failure probability), design robustness, and cost. The goal of the RGD of a shield-driven tunnel is to bring the safety state to an acceptable level, while maximizing the robustness and cost efficiency simultaneously. To this end, a multi-objective optimization is performed and a Pareto front is obtained, which provides a trade-off that may be used to select the most preferred design. Through an illustrative case, the effectiveness and significance of this new robust design methodology is demonstrated. 相似文献
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Accurate prediction of settlement for shallow footings on cohesionless soil is a complex geotechnical problem due to large uncertainties associated with soil. Prediction of the settlement of shallow footings on cohesionless soil is based on in situ tests as it is difficult to find out the properties of soil in the laboratory and standard penetration test (SPT) is the most often used in situ test. In data driven modelling, it is very difficult to choose the optimal input parameters, which will govern the model efficiency along with a better generalization. Feature subset selection involves minimization of both prediction error and the number of features, which are in general mutual conflicting objectives. In this study, a multi-objective optimization technique is used, where a non-dominated sorting genetic algorithm (NSGA II) is combined with a learning algorithm (neural network) to develop a prediction model based on SPT data based on the Pareto optimal front. Pareto optimal front gives the user freedom to choose a model in terms of accuracy and model complexity. It is also shown how NSGA II can be effectively applied to select the optimal parameters and besides minimizing the error rate. The developed model is compared with existing models in terms of different statistical criteria and found to be more efficient. 相似文献
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NPGA-GW在地下水系统多目标优化管理中的应用 总被引：7，自引：0，他引：7
在地下水系统管理问题中,涉及到多个相互冲突的目标函数常常被简化为不同形式的单一目标函数来求解,这种通过单一目标函数的优化方法只能给出一个解,由此确定的方案有时会违背决策者的意愿。而通过多目标优化方法可以得到一系列供决策者权衡选择的解集。将地下水流模拟程序MODFLOW 和溶质运移模拟程序MT3DMS 相耦合,采用基于小生境技术的Pareto 遗传算法进行求解,开发了一个用于地下水系统多目标管理的应用程序NPGA-GW。并将该程序应用于一个二维地下水污染修复问题的多目标优化求解,结果表明,该程序能够在较短的时间内得到一系列Pareto 最优解,解的跨度足够决策者进行适当的选择,具有很好的应用前景。 相似文献
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Yun Yang Jianfeng Wu Xiaomin Sun Jichun Wu Chunmiao Zheng 《Environmental Earth Sciences》2013,70(6):2481-2494
Two primary goals of a multi-objective evolutionary algorithm (MOEA) for solving multi-objective optimization problems are to find as many nondominated solutions as possible toward the true Pareto front and to maintain diversity of Pareto-optimal solutions along the tradeoff curves. However, few MOEAs can achieve these two goals concurrently. This study presents a new hybrid MOEA, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), in which the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions that arose from the evolving population of nondominated sorting genetic algorithm-II (NSGA-II). The NPTSGA coupled with a flow and transport model is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large field-scale groundwater remediation system for cleanup of large trichloroethylene plume at the Massachusetts Military Reservation in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface is incorporated into the NPTSGA to implement objective function evaluations in a distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world applications. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between the diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions. 相似文献
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Huiming Tang Wenping Gong Liangqing Wang C. Hsein Juang James R. Martin Changdong Li 《国际地质力学数值与分析法杂志》2019,43(7):1516-1536
Though the technology of using stabilizing piles to prevent landsliding is not new, the design of such piles with a meaningful optimization framework has been rarely reported. In this paper, a multiobjective optimization-based framework for design of stabilizing piles is presented, in which both reinforcement effectiveness and cost efficiency could be explicitly considered. The design parameters considered in the proposed design framework are the pile parameters, including pile diameter, spacing, length, and position, and the design objectives considered are the reinforcement effectiveness and cost efficiency. The design of stabilizing piles is then implemented as a multiobjective optimization problem. In that the desire to maximize the reinforcement effectiveness and that to maximize the cost efficiency are two conflicting objectives, the output of this multiobjective optimization will be a Pareto front that depicts a trade-off between these two design objectives. With the obtained Pareto front, an informed decision regarding the design of stabilizing piles is reached. The effectiveness and significance of the proposed multiobjective optimization-based design framework for stabilizing piles are demonstrated through two illustrative examples: one is the design of stabilizing piles in a one-layer earth slope and the other the design of stabilizing piles in a two-layer earth slope. Further, parametric analyses are conducted to investigate the influences of the pile design parameters on the stability of reinforced slopes. 相似文献
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将改进后的遗传算法GA（添加了小生境、Pareto解集过滤器等模块）与变密度地下水流及溶质运移模拟程序SEAWAT-2000相耦合,新开发了变密度地下水多目标模拟优化程序MOSWTGA。将MOSWTGA应用于求解大连周水子地区以控制抽水井所在含水层不发生海水入侵为约束的地下水开采多目标优化管理模型,得到地下水最大开采量与海水入侵面积之间一系列Pareto近似最优解。研究成果不仅为实行合理的地下水资源配置提供了科学的实用模型,同时也为解决多个优化目标下的变密度地下水优化管理问题提供高效可靠的模拟优化工具,具有重要的潜在环境经济效益。 相似文献
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Chandan Guria Mohan Varma Surya P. Mehrotra Santosh K. Gupta 《International Journal of Mineral Processing》2006
The binary-coded elitist non-dominated sorting genetic algorithm with the modified jumping gene operator (NSGA-II-mJG) is used to obtain global optimal solutions of flotation circuits. Several single-objective and multi-objective optimization problems are solved using the interconnecting cell linkage parameters (fraction flow rates) and the mean cell residence times as the decision variables. In the single-objective problem, the overall recovery of the concentrate stream is maximized for a desired grade of the concentrate. Two two-objective optimization problems are then solved. In one, the number of non-linking streams and the overall recovery of the concentrate are maximized simultaneously. This gives several simple circuits in a systematic manner with only marginally lower recoveries. In the other two-objective optimization problem, the overall recovery of the concentrate is maximized while the total cell volume is minimized. A three-objective problem (maximization of the overall recovery of the concentrate, maximization of the number of non-linking streams and minimization of the total cell volume) is then solved. All the problems constrain the grade of the product to lie at a fixed value. Finally, a complex and computationally intensive four-objective optimization problem is solved. The solution of several practical optimization problems in this study helps develop useful insights into the optimal solutions. 相似文献
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Flood events have the highest damage costs and losses among natural hazards. There are different types of measures to mitigate flood damage costs and their negative consequences. Application of flood-control reservoirs or detention dams, as one of the main measures, may decrease devastating flood effects or even may cause to intensify flood damages in the watershed by a poor design with tremendous construction costs. Optimal design of a flood-control multi-reservoir system can simultaneously minimize investment costs of constructions and potential flood damage costs. This study proposes a simulation-based optimization approach to optimize the design of multi-reservoirs for flood control in the watershed by coupling the MIKE-11 hydrodynamic model and the NSGA-II multi-objective optimization model. The present approach provides the Pareto optimal solutions between two conflict objectives of minimizing total investment costs and the expected flood damage costs in the watershed. Application of the proposed model for a small watershed in central part of Iran as a case study shows that optimal designs of multi-reservoir systems can efficiently reduce construction costs, flood peaks and their corresponding damage costs at the downstream reaches of the basin. 相似文献
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Lasse Hjuler Christiansen Andrea Capolei John Bagterp Jørgensen 《Computational Geosciences》2017,21(3):411-426
The uncertainties related to long-term forecasts of oil prices impose significant financial risk on ventures of oil production. To minimize risk, oil companies are inclined to maximize profit over short-term horizons ranging from months to a few years. In contrast, conventional production optimization maximizes long-term profits over horizons that span more than a decade. To address this challenge, the oil literature has introduced short-term versus long-term optimization. Ideally, this problem is solved by a posteriori multi-objective optimization methods that generate an approximation to the Pareto front of optimal short-term and long-term trade-offs. However, such methods rely on a large number of reservoir simulations and scale poorly with the number of objectives subject to optimization. Consequently, the large-scale nature of production optimization severely limits applications to real-life scenarios. More practical alternatives include ad hoc hierarchical switching schemes. As a drawback, such methods lack robustness due to unclear convergence properties and do not naturally generalize to cases of more than two objectives. Also, as this paper shows, the hierarchical formulation may skew the balance between the objectives, leaving an unfulfilled potential to increase profits. To promote efficient and reliable short-term versus long-term optimization, this paper introduces a natural way to characterize desirable Pareto points and proposes a novel least squares (LS) method. Unlike hierarchical approaches, the method is guaranteed to converge to a Pareto optimal point. Also, the LS method is designed to properly balance multiple objectives, independently of Pareto front’s shape. As such, the method poses a practical alternative to a posteriori methods in situations where the frontier is intractable to generate. 相似文献
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随着城市建设的发展,渣土边坡的数量和规模急剧增加,渣土边坡的防控研究受到了广泛关注。针对渣土边坡人工分层堆填建筑余渣土体参数的不确定性,采用预埋阻滑键加固渣土边坡的方式,提出了基于可靠度理论的阻滑键多目标优化设计方法。考虑不同阻滑键潜在组合对渣土边坡预估破坏损失的影响,将渣土边坡的预估破坏损失、稳定安全性和加固设计成本作为设计目标,通过多目标优化理论确定帕累托前沿并计算其关节点,获得预埋阻滑键加固边坡的最佳设计方案。以深圳市某渣土边坡为例,计算结果表明,将破坏概率作为衡量阻滑键加固渣土边坡效果指标时,应在渣土边坡前缘预埋两组尺寸长3 m、间距5 m的阻滑键加固边坡。采用上述阻滑键设计组合加固该渣土边坡时,可实现该边坡预估破坏损失、设计成本和稳定性达到最佳平衡。 相似文献
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This study shows the feasibility of obtaining hazardous hot spot information on landslide and debris flow from crowdsourced data. Historical hazard or disaster photographs were voluntarily uploaded by the public to a Web photograph album. A total of 2245 hazard photographs from 1973 to 2015 were crowdsourced, and each photograph was tagged with geographical coordinates. After the removal of outliers, 96% of the photograph points were found within the 4 km potential debris flow buffer of existing databases, and none was found along the steep slopes with a mean of 14°. The photograph hot spot analysis using local Moran’s I or G i * was identified statistically significant without subjective judgment. The DBSCAN model was also used to detect hot spot clusters effectively. The model parameters were nearly automatically generated on the basis of the count plot and the nearest neighbor distance graph. The results of these approaches were generally consistent with the hazardous hot spot maps and strongly related to central and southern Taiwan from the crowdsourced photograph data. Results reveal that the hot spot areas are found in areas with faults and near the potentially weak and fractured rocky regions. The majority of the landslides occur near the fault line because the strong ground motions triggered by an earthquake propagated along the fault rupture plane. Hot spot mapping using crowdsourced data can be used to estimate where debris flow will frequently occur and show how large the debris flow will be. Potentially hazardous areas can be effectively determined by the hot spot analysis of crowdsourced data. 相似文献