An improved genetic algorithm for searching for pollution sources

As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristics of high-speed computer calculation and conditions of the known relationship between the objective function and independent variables. There are several hundred generations of evolvement, but the functional relationship is unknown in pollution source searches. Therefore, the genetic algorithm cannot be used directly. Certain improvements need to be made based on the actual situation, so that the genetic algorithm can adapt to the actual conditions of environmental problems, and can be used in environmental monitoring and environmental quality assessment. Therefore, a series of methods are proposed for the improvement of the genetic algorithm： （1） the initial generation of individual groups should be artificially set and move from lightly polluted areas to heavily polluted areas; （2） intervention measures should be introduced in the competition between individuals; （3） guide individuals should be added; and （4） specific improvement programs should be put forward. Finally, the scientific rigor and rationality of the improved genetic algorithm are proven through an example.

Demarcation of potential seismic sources on integration of genetic algorithm and BP algorithm

In this paper potential seismic sources in coastal region of South China are identified by integration of genetic algorithm (GA) and back propagation (BP algorithm). GA is used for finding the best parameter combination rapidly in an infinite solution space for artificial neural networks (ANN). The results show that the distribution of potential seismic sources with different upper magnitude demarcated by this classifier is mostly satisfied the intrinsic relationship between seismic environment and earthquake occurrence, with less effect from subjective judgment of human being.

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

The linked simulation-optimization model can be used for solving a complex groundwater pollution source identification problem. Advanced simulators have been developed and successfully linked with numerous optimization algorithms for identification of groundwater pollution sources. However, the identification of pollution sources in a groundwater aquifer using linked simulation-optimization model has proven to be computationally expensive. To overcome this computational burden, an approximate simulator, the artificial neural network (ANN) model can be used as a surrogate model to replace the complex time-consuming numerical simulation model. However, for large-scale aquifer system, the performance of the ANN-based surrogate model is not satisfactory when a single ANN model is used to predict the concentration at different observation locations. In such a situation, the model efficiency can be enhanced by developing separate ANN model for each of the observation locations. The number of ANN models is equal to the number of observation wells in the aquifer. As a result, the complexity of the ANN-based simulation-optimization model will be related to the number of observation wells. Thus, this study used a modified formulation to find out the optimal numbers of observation wells which will eventually reduce the computational time of the model. The performance of the ANN-based simulation-optimization model is evaluated by identifying the groundwater pollutant sources of a hypothetical study area. The limited evaluation shows that the model has the potential for field application.

Multi-variable Optimization of HVAC System Using a Genetic Algorithm

Geothermal is a fast-growing alternative heat source for HVAC systems, however, the initial cost of using a ground source HVAC system is higher compared to an air source system. Studies about system design and operation are necessary to reduce the initial cost and ensure that the ground source heat pump system has high efficiency, resulting in a lower total life-time cost. In this study, a multi-variable evolutionary computation algorithm is proposed for generating optimal parameters for a geothermal source HVAC system. The system was modeled and simulated using MATLAB. The design parameters were calculated by minimizing the energy consumption, Based on an experimental building, a case study was presented. Using this model, the optimal set points were calculated and used as a designed system. Energy consumption of this system was reduced by about 10% compared to the system operated with a fixed supply cold water temperature （7 ℃）.

Cost-Effective Strategy for the Investigation and Remediation of Polluted Soil Using Geostatistics and a Genetic Algorithm Approach

The geostatistical technique of Kriging has extensively been used for the investigation and delineation of soil heavy metal pollution. Kriging is rarely used in practical circumstances, however, because the parameter values are difficult to decide and relatively optimal locations for further sampling are difficult to find. In this study, we used large numbers of assumed actual polluted fields (AAPFs) randomly generated by unconditional simulation (US) to assess the adjusted total fee (ATF), an assessment standard developed for balancing the correct treatment rate (CTR) and total fee (TF), based on a traditional strategy of systematic (or uniform) grid sampling (SGS) and Kriging. We found that a strategy using both SGS and Kriging was more cost-effective than a strategy using only SGS. Next, we used a genetic algorithm (GA) approach to find optimal locations for the additional sampling. We found that the optimized locations for the additional sampling were at the joint districts of polluted areas and unpolluted areas, where abundant SGS data appeared near the threshold value. This strategy was less helpful, however, when the pollution of polluted fields showed no spatial correlation.

Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm

This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis.

RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION

This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA)based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).

基于AquaCrop模型的水稻多目标灌溉制度优化研究

【目的】优化现有水稻灌溉制度,节约灌溉用水量,减少稻田氮磷流失量以降低面源污染风险。【方法】构建了基于AquaCrop模型和NSGA-Ⅱ算法的水稻灌溉制度模拟优化模型,利用水稻田间试验数据,开展以产量最大、氮磷流失量最小、灌水次数最少的3种目标组合(产量-氮磷流失量(Y-TNP)、产量-灌水次数(Y-N)、产量-氮磷流失量-灌水次数(Y-TNP-N))下的灌溉制度优化模型研究,提出适应不同生育期降水年型的稳产-控污-提效灌溉制度。【结果】(1)与常规灌溉相比,Y-TNP灌溉制度优化水稻产量下降2.14%,氮磷流失量减少23.09%;Y-N灌溉制度优化水稻产量下降1.76%,灌水次数减少53%;Y-TNP-N灌溉制度优化水稻产量下降2.64%,氮磷流失量减少22.83%,灌水次数减少2次。(2)不同典型年以Y-TNP-N为目标优化的稳产-控污-提效灌溉制度水稻产量介于7.74~7.78 t/hm^(2),同时大幅度减少灌水量进而降低氮磷流失量。【结论】AquaCrop模型可模拟试验区水稻的生长发育过程,本文构建的模拟-优化耦合模型可用于优化不同生育期降水年型下稳产-控污-提效的灌溉制度。

基于遗传-模式搜索算法的微尺度管控区域大气污染物PM2.5溯源

针对微尺度管控区域可能发生的大气污染提出有效的靶向诊断方法-结合高斯烟羽模型和遗传-模式搜索算法的大气污染物分布式溯源方法.将污染源反算模型得到的污染物理论质量浓度与传感器网络观测值的数据对应关系作为目标函数,使用模式搜索算法嵌入遗传算法加快反算模型的搜索过程,反算得到污染源强度和位置.依托杭州市亚运板球场馆大气感知器网络进行实验验证,监测2021年10月PM2.5质量浓度、气象数据,对所提出的混合式大气污染溯源方法进行实验验证.实验结果表明:改进遗传-模式搜索算法对于多维变量的搜索效果较好,能快速精准地反算污染源的位置和强度,可以为微尺度管控区域突发性气体污染防治提供应急决策参考.

基于P波到时拾取的分析法与遗传算法联合定位方法与应用

针对岩石力学震源定位问题,提出了一种在P波到时精确拾取的基础上通过分析法过滤异常到达传感器和遗传算法(genetic algorithms,简称GA)求解联合定位方法,并将其应用于岩石材料的破裂面定位以及采矿工程中。为了消除传感器到时精度、传统迭代法及迭代初值选择对定位的影响,基于时频分析的改进长短时窗(short-term average/long-term average,简称STA/LTA)法拾取P波到时,求解不同传感器组合分析解的逻辑概率密度函数过滤异常到达,引入遗传算法对到达清晰的震源进行定位。通过断铅、含夹层组合煤岩单轴压缩试验及定点爆破试验对该方法进行验证。结果表明:在对信号预先带通滤波后进行变分模态分解(variational mode decomposition,简称VMD)精确滤波的基础上结合改进STA/LTA法可以大幅提升到时拾取精度;通过分析法过滤异常到达对定位精度的影响,结合具有全局性、不受初值影响的遗传算法对震源定位,应用于岩石材料试验以及采矿工程中可以较准确的对震源进行定位,该方法较传统迭代法、线性定位法等更具有工程实际应用价值。

基于用电数据的潍坊市工业污染源冬奥会管控效果评估

利用潍坊市安装用电监控的工业企业在冬奥会前和会中的生产用电数据及部分企业大气污染物排放在线监测数据,分析企业生产用电量和排放量的变化特征及潜在的相关性,评估管控措施落实状况及减排效果。结果表明:管控期间,潍坊市工业企业用电量相比管控前平均下降57.96%;完全停产类企业用电量下降75%以上,大气污染物(SO_(2)、NO_(x)、烟粉尘)排放量下降100%;限产类企业用电量降幅呈现随管控措施加严而增大的趋势;延迟复产类企业延迟复产率为47.83%~96.00%,用电量下降50.76%。根据管控期间各行业企业的用电量变化状况,判定各企业基本落实了减排要求。可见,企业用电数据在一定情况下,可以替代或辅助工业企业排放数据,反映企业管控措施落实状况及减排效果。

多谐波源接入的城市配电网SAPF优化配置方法

针对分布式光伏和电动汽车充电设施等新谐波源渗透率高及分散接入造成的城市配电网谐波污染分散化与全网化问题,构建一种兼顾配电网谐波治理成本与治理成效的并联型有源电力滤波器选址定容双层优化模型。首先,外、内层模型分别以配电网谐波治理成本最小和电压畸变率最低为目标函数;然后,采用粒子群优化算法和遗传算法分别求解,实现配电网谐波治理效益最高的目标;最后,选取IEEE 33节点系统进行仿真,验证了所建模型对配电网谐波治理装置优化配置的有效性。

严寒地区地源热泵供热系统运行优化研究

地源热泵供热系统在运行控制方案上的优化,是建筑节能降耗的重要途径。以沈阳市ST办公楼地源热泵系统为研究对象,实地调研获取能源系统运行参数,归纳地源热泵的运行特性,锁定实际建筑中地源热泵系统供热现存问题,利用遗传算法寻求地源热泵系统最优运行控制参数。利用TRNSYS模拟能源系统运行,通过比较系统原有的改变机组负载率的控制方案与优化后最优运行控制参数方案的系统耗电量和性能系数,预测最佳控制方案下地源热泵系统的优化效果。结果表明:在最优运行控制方案下,地源热泵系统性能显著提升,系统耗电量降低14.5%,系统性能系数(COP)提升23.8%,热泵机组耗电量降低15.1%,地源热泵系统运行更加高效节能。

一种新的相干信源DOA估计算法:加权空间平滑协方差矩阵的Toeplitz矩阵拟合

文献 [1]提出的最优加权空间平滑技术可以使相干源存在时的信源协方差矩阵恢复为对角阵 .由于文献 [1]中导出的最优权矩阵是空间信源方位的函数矩阵 ,本文利用最优加权空间平滑后阵列协方差矩阵的Toeplitz性 ,构造了一个全新的优化拟合的代价函数 ,并基于此提出了一种相干源方位估计的新算法 .与文献 [1]不同 ,算法的实现不需要方位估计的先验知识和协方差矩阵的去噪预处理 .分辨性能的蒙特卡罗仿真实验表明 ,新算法对空间相干信源的分辨性能优于常规的空间平滑算法和最大似然算法 ,在小阵列和信源空间间隔较近时 ,算法的优越性尤为突出 .

基于集成替代模型和遗传算法的地下水污染源信息识别

准确识别污染源信息是高效治理和修复地下水污染的前提。为解决使用传统模拟优化方法识别污染源信息过程中产生的严重计算负担问题,文章首先建立BP神经网络(back propagation neural network,BPNN)模型、支持向量回归(support vector regression,SVR)模型和核极限学习机(kernel extreme learning machine,KELM)模型代替传统模拟优化方法中的地下水水流和溶质运移模拟模型。然后,使用简单平均法和遗传算法(genetic algorithm,GA)计算权重值并建立集成替代模型进一步提高模型精度。最后,将表现最优的基于遗传算法建立的集成替代模型嵌入识别污染源信息的优化模型中。算例结果分析表明,嵌入基于遗传算法建立的集成替代模型的优化模型相较于传统模拟优化模型计算时间由11 d大幅度缩短至39 min,且求解所得的污染源信息参数接近真实值,可用于解决污染源信息识别问题。

基于PSO-SA算法的源项反演方法研究

针对大气污染事故突发时,事故发生点无法确定或人员不能接近的情况,研究了基于环境监测数据源项反演以获取事故源项数据的技术,设计实现了一种基于粒子群-模拟退火源项反演方法。采用自适应方法调整惯性权重系数,与高斯烟羽扩散模型结合,对事故源项数据进行反演。实验结果显示:在所选监测点监测数据的反演实验中,基于粒子群-模拟退火算法(PSO-SA)结合了两种算法的优势,能够获得与期望值较为符合的反演结果。进一步分析了监测点数据误差及监测点数量对反演结果的影响,并将文中方法与粒子群算法(PSO)进行对比,同等条件下,较粒子群算法精度提高了8%,能够快速实现对大气污染源强和位置的准确估计。

高速公路风光储能系统源荷匹配及容量配置优化

建立EV充电负荷预测模型,采用蒙特卡洛模拟法预测EV充电负荷时间分布特性,提出考虑充电桩参数和充换电模式的源荷匹配优化策略,建立风光储能系统综合评价体系,通过改进遗传算法进行规划求解,得到最优容量配置。以山东省某服务区为例进行实证研究。结果表明:EV充电负荷预测曲线呈现日间“双峰”形态,为使负荷用能曲线向光伏发电曲线贴近,合理设置服务区充电桩数量和最大充电功率,并选择换电参与的充换电模式,改进遗传算法得到的最优化配置方案贴近度相对于初始规划方案有所提升,可实现储能系统的充能和余电的上网。

基于自适应多保真度Co-Kriging代理模型的地下水污染源反演识别

为高效率高精度地进行地下水污染源反演识别,综合运用高保真度和低保真度地下水溶质运移数值模拟模型,研究应用集成差分进化算法的Co-Kriging方法建立模拟模型的多保真度代理模型;在此基础上,探索应用马尔科夫链蒙特卡洛(MCMC)-DREAM_((D))算法,并采用自适应更新多保真度代理模型策略进行地下水污染源反演识别.为验证上述方法的有效性和可行性,开展了数值算例研究.结果表明:相比仅基于高保真度模型输入-输出样本构建的Kriging代理模型,联合运用高保真度和低保真度模型输入-输出样本构建的Co-Kriging代理模型对模拟模型的逼近精度更高;耦合多保真度Co-Kriging代理模型和MCMC-DREAM_((D))算法能够得到较高精度的污染源反演结果,且能够大幅度减小计算负荷;同时,采用自适应更新多保真度代理模型策略能够进一步提高污染源反演识别精度.

基于PCA-GA-RF的矿井突水水源快速识别模型

矿井突水已成为影响矿山安全生产的主要危害之一,快速准确识别突水水源类型是矿井突水灾害治理的关键步骤。提出了1种基于PCA-GA-RF的矿井突水水源识别模型;基于安徽省颍上县谢桥煤矿的88组水样实测数据,遵循分层随机抽样的原则,按照7∶3的比例将其分为62组训练样本和26组预测样本,经PCA提取4个主成分,构建PCA-GA-RF模型,并与PCA-RF、PCA-ABC-RF和PCA-FA-RF模型对比。结果表明:PCA-GA-RF模型判别结果准确率为96.153 8%,与其他模型相比准确率、精确率、召回率和F1值(精确召回率)最高,具有优越性。