基于谐振子遗传算法的高效地下水优化管理模型

在传统遗传算法和模拟谐振子算法的基础上,结合两者的优点,提出了一种新型快速高效的谐振子遗传算法。通过一个理想的水资源管理模型的算例和一个华北平原典型区地下水资源优化的实际算例,从寻优结果和寻优效率两个方面对谐振子遗传算法、传统遗传算法和模拟谐振子算法进行了对比分析。在两个地下水管理模型中,与传统的遗传算法和模拟谐振子算法相比,新型的谐振子遗传算法搜索效率达到模拟谐振子算法搜索效率的2倍以上,得到的最优解比遗传算法所得到的最优解分别增加供水量1.1×10³ m³/d和0.47×10⁸ m³/a,说明谐振子遗传算法具有更强的全局搜索能力和更好的寻优效率。     

水文地质参数识别的连续禁忌搜索改进算法

水文地质参数的正确与否是构建地下水数值模型的根本,而参数寻优结果很大程度上取决于优化算法的选择。禁忌搜索算法是一种广泛应用于组合优化问题的启发式全局寻优算法,但在连续函数优化领域应用比较少。基于上述考虑,本文首先引入求解连续函数优化问题的连续禁忌搜索算法并对其进行改进,进而提出一种连续禁忌搜索改进算法(ICTS),最后将其与地下水模型耦合进行水文地质参数识别。算例研究表明,ICTS算法较其他算法(CTS,SGA,Micro-GA,PSO)求解效率提高1.87～4.64倍,求解精度提高1.08～12.86倍。因此ICTS算法在参数反演计算中求解精度高、收敛速度快、寻优性能强,是一种值得推广的水文地质参数识别方法。     

基于INPGA的地下水污染治理多目标优化管理模型:Ⅱ——实例应用

简单算例研究表明改进的小生境Pareto遗传算法(INPGA)用于求解地下水系统的多目标优化管理模型时,求解过程简单,计算速度快,而且得到的Pareto解集跨度更为合理.本文以美国麻省军事保护区(Massachusetts Military Reservation,MMR)为实例,通过建立研究区复杂地下水污染治理的多目...     

双种群进化粒子群算法求解地下水管理模型

为避免粒子群算法(PSO)早熟的缺点,设计了一种双种群进化粒子群算法(DE-PSO)。DE-PSO是基于PSO,引入选择、交叉及差分变异操作,并结合合理有效的粒子评价方法及越界处理方法之后形成的。将DE-PSO应用于两个地下水管理模型算例,第一个算例DE-PSO解的总抽水量分别比遗传算法(GA)、模拟退火算法(SA)和PSO减少了64、256、207 m3/d,第二个算例DE-PSO解的总治理成本分别比GA、SA和PSO减少了57.74、151.93、76.59万元。两个算例中DE-PSO都表现出稳定的进化趋势,寻优效率好于GA、SA和PSO,可以有效求解地下水管理模型问题。     

分布参数地下水管理模型的遗传算法研究

在简单回顾了分布参数地下水管理模型的发展和建模方法基础上, 阐述了将地下水模拟模型耦合到遗传算法之中, 求解分布参数地下水管理模型的方法和特点, 给出了该算法的程序框图。最后通过实例说明遗传算法适于求解非线性地下水管理模型。     

地下水管理模型求解方法综述

地下水管理模型求解方法的研究是目前地下水管理领域的热点问题。本文从地下水管理模型传统优化算法和现代智能优化算法等方面进行了评述,着重讨论了目前应用较广泛的求解非线性地下水系统的优化算法,如遗传算法、模拟退火算法、人工神经网络算法等;阐述了地下水监测网优化设计研究以及多目标地下水管理模型的求解方法。最后指出应加强地下水动态规划管理模型和地下水系统随机管理模型的求解技术的研究。     

改进的模拟退火遗传算法在地下水管理中的应用

对于高度非线性、非凸的地下水管理模型,传统优化方法难以找到全局最优解。本文采用模拟退火遗传算法求解地下水管理模型,并从三个方面对算法进行改进:引入小生境技术,采用自适应交叉和变异概率,在选择过程中采用最优保存策略,从而提高算法的全局寻优能力和收敛速度。采用惩罚函数法处理约束条件。用Fortran 90语言编制了计算程序,并通过Schaffer测试函数验证了该算法不仅具有强大的全局寻优能力和局部搜索能力,而且具有较快的收敛速度和较高的优化精度。将该算法应用到某研究区地下水管理中,取得了较好的效果。     

地下水疏干井群优化设计的遗传算法

讨论了用遗传算法求解优化问题的基本原理、参数的确定方法及解题的基本步骤。通过对假设疏干井群优化设计管理模型的计算,讨论了遗传算法在地下水疏干井群优化设计中应用的有效性和优越性。     

改进的遗传算法在地下水数值模拟中的应用

地下水流数值模拟中的模型识别问题，可以转化为函数的最优化问题。鉴于遗传算法的特点，将之引入到地下水流数值法中，用以解决地下水数学模型的识别问题。在建立地下水数值模拟中模型识别问题的是优化模型后，采取将最优化模型中的目标函数嵌入到遗传算法适应度函数中的方法，实现遗传算法与地下水流数值法的耦合。基于优化模型和遗传算法的运算过程，编写计算程序，实现地下水数学模型的自动识别。根据在珲春盆地地下水资源评价实例中应用得到的结果，信纸证了改进的遗传算法在地下水数值模拟中应用的可行性与有效性。     

基于GA-SVR的渗透系数参数反演方法

渗透系数参数反演的本质是优化问题求解,遗传算法是一种基于自然选择和群体遗传机理的新的全局优化求解方法,可以较好地用于求解诸如渗透系数参数反演等复杂非线性组合优化问题。基于结构风险最小化原理的支持向量机具有逼近复杂非线性系统、较强的学习泛化能力,可以用来计算渗透系数参数反演过程中的测点水头值。实验表明,基于遗传算法-支持向量回归机的地下水渗透系统参数反演拟合效果良好,能大大提升区间搜索效率,避免出现局部最优解,其参数识别精度符合实际应用要求。     

A Hybrid Multi‐Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm (GA) with a TS based MOEA, the niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.     

Spatio-temporal optimisation of agricultural drainage using groundwater models and genetic algorithms: an example from the Murray Irrigation Area,Australia

Drainage schemes for salinity management are aimed at lowering the shallow groundwater to help increase production and reduce ecological risks. Once the groundwater levels are lowered to desired agro-ecological thresholds, the drainage scheme’s operation needs to be optimised according to the spatio–temporal variation in groundwater dynamics. Groundwater systems can be modelled if their behaviour is fully known and understood but a key difficulty in optimisation is dealing with non-linear and non-unique spatio-temporal problems. Such problems can be optimised using genetic algorithms (GA) aimed at finding near optimal solutions to highly non-linear optimisation problems. The major advantages of GAs are their broad applicability, flexibility and their ability to find solutions with relatively modest computational requirements. A surface water/groundwater interaction model has been developed in conjunction with GA based spatio-temporal optimisation of pumping operation of a subsurface drainage scheme. The aim has been to achieve a similar or better than on-going level of service both in space and time domains. The Wakool Tullakool Subsurface Drainage Scheme in the Murray Irrigation Area, Australia is discussed to illustrate the modelling process. The model results are being used to plan the cost-effective operation of the tubewells to control water logging and salinisation.     

大连周水子海水入侵区地下水多目标优化管理模型

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。     

NPGA-GW在地下水系统多目标优化管理中的应用

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。     

Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models

The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non-regularity of the objective function. In this paper, we propose an optimization methodology for determining optimal well locations and trajectories based on the covariance matrix adaptation evolution strategy (CMA-ES) which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. In addition, to improve the optimization procedure, two new techniques are proposed: (a) adaptive penalization with rejection in order to handle well placement constraints and (b) incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function. The approach is applied to the PUNQ-S3 case and compared with a genetic algorithm (GA) incorporating the Genocop III technique for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, and standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic algorithm: It leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations needed to reach a good well configuration. Moreover, coupling CMA-ES with a meta-model leads to further improvement, which was around 20% for the synthetic case in this study.     

遗传算法在非线性地下水经济管理模型中的应用

通常采用基于梯度的数学规划方法求解地下水管理模型,如线性规划和非线性规划。但对于高度非线性、非凸的优化问题,尤其是涉及到经济或环境的地下水管理模型,传统方法难以有效地寻找全局最优解。本文介绍了一种求解非线性地下水资源管理模型的遗传算法,并以山东羊庄盆地分布参数地下水系统非线性管理模型为例,给出了用遗传算法在求解这类问题的一般步骤。结果表明该方法能快速有效地找到全局最优解。     

Optimizing groundwater development strategies by genetic algorithm: a case study for balancing the needs for agricultural irrigation and environmental protection in northern China

Gaoqing Plain is a major agriculture center of Shandong Province in northern China. Over the last 30 years, the diversion of Yellow River water for intensive irrigation in Gaoqing Plain has led to elevation of the water table and increased evaporation, and subsequently, a dramatic increase in salt content in soil and rapid degradation of crop productivity. Optimal strategies have been explored, that will balance the need to extract sufficient groundwater for irrigation (to ease the pressure on diverting Yellow River water) with the need to improve the local environment by appropriately lowering the water table. Two simulation-optimization models have been formulated and a genetic algorithm (GA) is applied to search for the optimal groundwater development strategies in Gaoqing Plain, while keeping the adverse environmental impacts in check. Compared with the trial-and-error approach of previous studies, the optimization results demonstrate that using an optimization model coupled with a GA search is both effective and efficient. The optimal solutions identified by the GA will provide Gaoqing Plain with the blueprints for developing sustainable groundwater abstraction plans to support local economic development and improve its environmental quality. Li Zheng is currently a visiting scientist in the Mathematical Modeling and Analysis Group, Los Alamos National Laboratory, New Mexico, USA.