Nonlinear amplitude inversion using a hybrid quantum genetic algorithm and the exact zoeppritz equation

The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high accuracy in modelling the reflection coefficients.However,amplitude inversion based on it is highly nonlinear,thus,requires nonlinear inversion techniques like the genetic algorithm(GA)which has been widely applied in seismology.The quantum genetic algorithm(QGA)is a variant of the GA that enjoys the advantages of quantum computing,such as qubits and superposition of states.It,however,suffers from limitations in the areas of convergence rate and escaping local minima.To address these shortcomings,in this study,we propose a hybrid quantum genetic algorithm(HQGA)that combines a self-adaptive rotating strategy,and operations of quantum mutation and catastrophe.While the selfadaptive rotating strategy improves the flexibility and efficiency of a quantum rotating gate,the operations of quantum mutation and catastrophe enhance the local and global search abilities,respectively.Using the exact Zoeppritz equation,the HQGA was applied to both synthetic and field seismic data inversion and the results were compared to those of the GA and QGA.A number of the synthetic tests show that the HQGA requires fewer searches to converge to the global solution and the inversion results have generally higher accuracy.The application to field data reveals a good agreement between the inverted parameters and real logs.

Development and Application of a Modified Genetic Algorithm for Estimating Parameters in GMA Models

In this work we introduce a modified version of the simple genetic algorithm (MGA) and will show the results of its application to two GMA power law models (a general theoretical branched pathway system and a mathematical model of the amplification and responsiveness of the JAK2/STAT5 pathway representing an actual, experimentally studied system). The two case studies serve to illustrate the utility and potentialities of the MGA method for concerning parameter estimation in complex models of biological significance. The analysis of the results obtained from the application of the MGA algorithm allows an evaluation of the potentialities and shortcomings of the proposed algorithm when compared with other parameter estimation algorithm such as the simple genetic algorithm (SGA) and the simulated annealing (SA). MGA shows better performance in both studied cases than SGA and SA, either in the presence or absence of noise. It is suggested that these advantages are due to the fact that the objective function definition in the MGA could include the experimental error as a weight factor, thus minimizing the distance between the data and the predicted value. Actually, MGA is slightly slower that the SGA and the SA, but this limitation is compensated by its greater efficiency in finding objective values closer to the global optimum. Finally, MGA can lead to an early local optimum, but this shortcoming may be prevented by providing a great population diversity through the insertion of different selection processes.

Underground water quality model inversion of genetic algorithm

The underground water quality model with non-linear inversion problem is ill-posed, and boils down to solving the minimum of nonlinear function. Genetic algorithms are adopted in a number of individuals of groups by iterative search to find the optimal solution of the problem, the encoding strings as its operational objective, and achieving the iterative calculations by the genetic operators. It is an effective method of inverse problems of groundwater, with incomparable advantages and practical significances.

Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm

The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update the weights of neural networks. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the proposed method improves considerably the precision of the inverse kinematics solutions for robot manipulators and guarantees a rapid global convergence and overcomes the drawbacks of SGA and the BP algorithm.

Design of a Computational Heuristic to Solve the Nonlinear Liénard Differential Model

In this study,the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks(ANNs)along with the hybridization procedures of global and local search approaches.The global search genetic algorithm(GA)and local search sequential quadratic programming scheme(SQPS)are implemented to solve the nonlinear Liénard model.An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS.The motivation of the ANN procedures along with GA-SQPS comes to present reliable,feasible and precise frameworks to tackle stiff and highly nonlinear differentialmodels.The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models.The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness,viability and efficacy.Moreover,statistical performances based on different measures are also provided to check the reliability of the ANN along with GASQPS.

A Novel Training Algorithm of Genetic Neural Networks and Its Application to Classification

First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.

Study on Multi-stream Heat Exchanger Network Synthesis with Parallel Genetic/Simulated Annealing Algorithm

The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS.

基于遗传算法的磨削力模型系数优化及验证

在磨削力模型求解问题中,目前大多使用分段计算法或列方程组直接计算各个待求系数,不仅计算量大且其精度也无法保证。另外,传统的回归模型容易陷入局部最优,难以描述非线性关系。为此,将遗传算法引入到非线性优化函数参数优化中,基于外圆横向磨削力模型、平面磨削力模型、外圆纵向磨削力模型等现有的模型数据,开展磨削力理论模型的系数优化方法研究。相关性分析结果表明:通过计算得到的3种模型磨削力的预测精度提高了14.69%~42.54%,且3种模型所预测的法向磨削力的平均误差分别为5.9%、9.13%、3.23%,切向力平均误差分别为6.78%、8.36%、3.69%。经对比知,优化后的模型拟合度较好,模型预测精度显著提高。遗传算法优化后的非线性优化函数GA-LSQ算法更适合磨削力模型的求解,可对磨削力的预测及实际加工生产中的参数优化提供参考。

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.

遗传算法在橡胶超弹性材料参数反求中的应用

将优化算法与有限元仿真分析相结合可以对橡胶的材料参数进行反求,该方法基于一款叠层橡胶弹簧的试验数据,并选用Mooney-Rivlin超弹性本构模型。在参数反求的过程中,为解决传统遗传算法出现的早熟、稳定性差以及收敛速度慢的问题,将编码方式改为实数编码,并在算法中引入精英保留策略和自适应的交叉与变异概率。将根据改进遗传算法反求得到的刚度特性曲线与试验结果进行对比,发现二者具有很好的一致性,表明通过反求得到的材料参数能准确地对橡胶材料的力学性能进行描述。

基于改进遗传算法的无线传感器网络覆盖优化

提出一种基于逆模型引导算法搜索的多目标演化算法(MOEA-OMG),通过对种群的目标空间随机采样,然后利用高斯过程将采样解映射回决策空间,得到包含种群分布信息的试验解,引导算法搜索,利用提出的重组算子将试验解与其他解个体进行组合,产生高质量后代解。将算法应用到解决无线传感器网络(WSNs)覆盖问题,并与传统的几种优化算法进行实验对比,结果表明,所提算法在求解WSNs覆盖问题时,展现出较为明显的性能优势。

基于阻抗模型的玻璃棉声学参数反演方法研究

为了能准确获取玻璃棉材料的声学参数,文章对玻璃棉声学参数在不同阻抗模型下的声学参数进行了反演。采用了厚度分别为22 mm和44 mm的玻璃棉样本实测吸声曲线及各声学参数,选取四种常用阻抗模型,通过遗传算法(Genetic Algorithm,GA)对玻璃棉材料进行声学参数的反演,并选择反演效果最优的模型进行敏感性分析。比较各参数反演结果的误差比,并对比不同模型描述的吸声曲线与测试曲线的一致性,最后量化并比较Johnson-Champoux-Allard(JCA)模型中各参数对吸声系数的影响程度。研究表明,使用GA结合JCA模型或Johnson-Champoux-Allard-Lafarge(JCAL)模型反演的参数值与测试值误差较小;JCA模型适用于玻璃棉材料的声学参数反演,模型中流阻率和曲折度的敏感性较高,反演过程需保证其精确度。

基于混合遗传算法的可变尺寸货物装箱问题研究

目的针对冷链运输中的生鲜打包及装载优化问题,提出一种允许货物以体积恒定为前提进行尺寸变化的包装装载方案,以最大化集装箱的空间利用率。方法基于上述问题,构建非线性混合整数规划模型,为了方便CPLEX或LINGO等求解器对该非线性混合整数规划模型进行求解,采用一种分段线性化方法,将该非线性模型进行线性化处理。由于所研究问题具有NP-hard属性,无论是CPLEX还是LINGO都无法有效求解大规模算例,因此设计一种有效结合遗传算法与深度、底部、左部方向优先装载(Deepest bottom left with fill,DBLF)的算法。结果大小规模算例实验验证结果表明,混合遗传算法能够在合理时间内获得最优解或近似最优解。结论所提出的可变尺寸包装方案有效提高了装载率,有益于客户和物流公司。

多目标遗传算法反演对流层大气温湿廓线研究

针对地基微波辐射计反演对流层大气温湿廓线完全依赖于历史数据的问题,设计了一种新的反演算法和流程。在对历史探空数据的统计分析基础上,得到各层大气温湿参数经验范围。从经验库中随机构造一条大气温湿廓线作为初值,基于大气微波辐射传输模式MonoRTM模型,计算模拟亮温。比较计算值与实测亮温的接近程度,通过更新初值,不断迭代计算,逐步筛选出可行解,并通过设定大气温湿垂直递减率等约束条件,从可行解中约束出帕累托前沿。采用皮尔逊系数加权平均的方法,从帕累托前沿中得到全局满意解。研究结果表明,新建立的基于多目标遗传算法的不完全依赖于历史数据的对流层大气温湿廓线反演模型,有较好的自适应能力和鲁棒性,反演精度高。该模型可以满足微波辐射计在历史气象资料积累匮乏地区的使用需求。

GA-Based Model Predictive Control of Semi-Active Landing Gear

Semi-active landing gear can provide good performance of both landing impact and taxi situation, and has the ability for adapting to various ground conditions and operational conditions. A kind of Nonlinear Model Predictive Control algorithm （NMPC） for semi-active landing gears is developed in this paper. The NMPC algorithm uses Genetic Algorithm （GA） as the optimization technique and chooses damping performance of landing gear at touch down to be the optimization object. The valve＇s rate and magnitude limitations are also considered in the controller＇s design. A simulation model is built for the semi-active landing gear＇s damping process at touchdown. Drop tests are carried out on an experimental passive landing gear systerm to validate the parameters of the simulation model. The result of numerical simulation shows that the isolation of impact load at touchdown can be significantly improved compared to other control algorithms. The strongly nonlinear dynamics of semi-active landing gear coupled with control valve＇s rate and magnitude limitations are handled well with the proposed controller.

Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19

The present investigations are associated with designing Morlet wavelet neural network(MWNN)for solving a class of susceptible,infected,treatment and recovered(SITR)fractal systems of COVID-19 propagation and control.The structure of an error function is accessible using the SITR differential form and its initial conditions.The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm(GA)and active-set algorithm(ASA),i.e.,MWNN-GA-ASA.The detail of each class of the SITR nonlinear COVID-19 system is also discussed.The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method.The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method.The plots of the absolute error,convergence analysis,histogram,performancemeasures,and boxplots are also provided to find the exactness,dependability and stability of the MWNN-GA-ASA.

基于遗传算法的水下全断面砂岩盾构隧道荷载反演分析

盾构隧道结构设计中,是否能够较为准确地得到作用在管片衬砌上的荷载十分关键;然而,现场实测实施较为困难,会受到环境和施工因素的影响,而荷载反演是一个较好的得到作用于衬砌上水土荷载的方法。依托佛莞城际铁路狮子洋隧道工程,提出适用于硬岩荷载模式的修正梁-弹簧模型,并结合修正模型对隧道水下全断面处于中风化泥质砂岩时进行荷载反演。比较遗传算法、现场实测和太沙基理论计算的荷载值以及由荷载计算出的内力值,反演得到的中风化泥质砂岩侧压力系数为0.38,基床系数为242.43 MPa/m。结果表明:(1)在该断面下,采用修正模型较为合适,且遗传算法得到的荷载能够较好拟合实测内力值;(2)作用在衬砌上的荷载主要为水压力,荷载计算应采用水土分算;(3)地勘报告中中风化泥质砂岩围岩参数偏小,实际取值可参考遗传算法反演值;(4)采用太沙基理论的荷载计算得到的弯矩值偏大,轴力值偏小,结果偏向保守。

基于GPR代理模型和GA-APSO混合优化算法的软基水闸底板脱空反演

软基水闸底板脱空是水闸在长期服役期间受水流侵蚀等环境因素影响所产生的一种危害极大且难以察觉的病害。由于其病害部位于水下,传统方法难以检测,该研究提出一种基于高斯过程回归(Gaussian process regression,GPR)代理模型和遗传-自适应惯性权重粒子群(genetic algorithm-adaptive particle swarm optimization,GA-APSO)混合优化算法的水闸底板脱空动力学反演方法,用于检测软基水闸底板脱空。首先,构建表征软基水闸底板脱空参数和水闸结构模态参数之间非线性关系的GPR代理模型;其次,基于GPR代理模型与水闸实测模态参数建立脱空反演的最优化数学模型,将反演问题转化为目标函数最优化求解问题;最后,为提高算法寻优计算的精度,提出一种GA-APSO混合优化算法对目标函数进行脱空反演计算,并提出一种更合理判断反演脱空区域面积和实际脱空区域面积相对误差的指标—面积不重合度。为验证所提方法性能,以一室内软基水闸物理模型为例,对两种不同脱空工况开展研究分析,结果表明,反演脱空区域面积和模型实际设置脱空区域面积的相对误差分别为8.47%和10.77%,相对误差值较小,证明所提方法能有效反演出水闸底板脱空情况,可成为软基水闸底板脱空反演检测的一种新方法。

面向舰艇编队的无人机集群物资补给规划研究

面向多约束条件下的舰艇编队物资补给规划任务,本文综合考虑无人机集群在执行补给任务时的作业以及惩罚时间成本,设计了规划模型的优化目标函数,并以舰艇补给时间窗、无人机最大载重量等为约束条件,构建了物资补给规划模型.为了解决自适应遗传算法(AGA)易陷入局部最优的问题,设计了非线性概率函数用于确定交叉概率和变异概率,并提出了一种基于非线性自适应遗传算法的无人机物资补给规划方法(NLAGA).实验结果表明:在标准算例Solomen求解问题上NLAGA方法优于AGA和改进的AGA方法;在最优的补给时间成本条件下,NLAGA方法能有效地实现舰艇编队的物资补给任务中无人机集群数量确定和飞行路径规划.

汽车转向非线性平衡点遗传算法求解及其改进

针对汽车转向非线性平衡点求解问题,研究了遗传算法求解效果并提出改进方法.建立汽车转向二自由度模型,说明汽车转向非线性平衡点只能数值迭代求解的原因,构造适于智能优化方法的适应度函数,提出了确定可行求解范围的方法.在车速70 km/h、路面附着系数0.5和前轮转角0~0.2 rad的行驶条件下,应用遗传算法求解得到3个平衡点.通过比较大小两个转角的适应值曲面,说明遗传算法求解小转角平衡点不满足精度的原因,提出了遗传算法与BFGS(broyden-fletcher-goldfarb-shanno)拟牛顿法融合的求解流程.结果表明:融合求解流程可以求解满足精度要求的小转角平衡点,求解效率高于遗传算法,弥补了遗传算法单独求解的不足.