Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.

APPLICATION OF INTEGER CODING ACCELERATING GENETIC ALGORITHM IN RECTANGULAR CUTTING STOCK PROBLEM

An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm （AGA） based on integer coding and AGA＇s detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.

Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology

In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.

The application of projection pursuit classification in the process of strategy selection and evaluation based on the real coded accelerating genetic algorithm

During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.

Real-Code Genetic Algorithm for Ground State Energies of Hydrogenic Donors in GaAs-（Ga,Al）As Quantum Dots

We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter or solving an eigenequation, so the present method overcomes the major difficulties of the variational method. RGAs also do not require coding and encoding procedures, so the computation time and complexity are reduced. The ground state energies of hydrogenic donors in GaAs-(Ga,Al)As quantum dots have been calculated for a range of the radius of the quantum dot radii of practical interest. They are compared with those obtained by the variational method. The results obtained demonstrate the proposed method is simple, accurate, and easy implement.

Adaptive Real-Coded Genetic Algorithm for Identifying Motor Systems

In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.

Design of S-band photoinjector with high bunch charge and low emittance based on multi-objective genetic algorithm

High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light sources.The bunch charge and RMS bunch length at the exit of the LINAC play a crucial role in the peak current;the minimum transverse emittance is mainly determined by the injector of the LINAC.Thus,a photoin-jector with a high bunch charge and low emittance that can simultaneously provide high-quality beams for 4th generation synchrotron radiation sources and FELs is desirable.The design of a 1.6-cell S-band 2998-MHz RF gun and beam dynamics optimization of a relevant beamline are presented in this paper.Beam dynamics simulations were performed by combining ASTRA and the multi-objective genetic algorithm NSGA II.The effects of the laser pulse shape,half-cell length of the RF gun,and RF parameters on the output beam quality were analyzed and compared.The normalized transverse emittance was optimized to be as low as 0.65 and 0.92 mm·mrad when the bunch charge was as high as 1 and 2 nC,respectively.Finally,the beam stability properties of the photoinjector,considering misalignment and RF jitter,were simulated and analyzed.

Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

基于代理遗传优化的智能驾驶系统加速测试方法

提出了一种基于代理遗传优化的智能驾驶系统加速测试方法。首先,通过场景要素层次分析权值与优解区域特征改进参数采样模块中的拉丁超立方采样区间,实现了采样效率与优化效果的协同提升;其次,利用参数采样结果和重复度筛选机制增加遗传寻优模块的种群多样性,克服了传统遗传算法的局部收敛难题;然后,利用基于循环更新机制的代理筛选模块对场景测试结果进行预测,平衡了加速算法与代理模型应用之间的效率与精度矛盾;最后,搭建仿真平台在高维时序分解的前车变速场景下对待测智能驾驶系统进行加速测试与验证。结果表明,本文提出的方法可有效搜寻大量关键场景并提升测试效率。

数字乡村建设对防返贫能力的影响及空间效应研究

防止规模性返贫是扎实推进乡村振兴的关键所在,数字乡村建设对提升农村防返贫能力具有重要意义。本文基于2016~2022年中国30个省(区、市)的面板数据,运用基于加速遗传算法的投影寻踪模型测度数字乡村建设水平与防返贫能力,通过核密度估计刻画两者的时空分异特征,利用空间杜宾模型深入分析数字乡村建设对防返贫能力的空间溢出效应。研究发现:2016~2022年,数字乡村建设水平与防返贫能力整体上在不断提高,数字乡村建设存在多极化趋势,防返贫能力两极化现象逐渐减弱。农村防返贫能力具有显著的空间聚集特征,主要表现为“高—高”和“低—低”聚集模式。空间杜宾溢出效应分解表明:数字乡村建设对防返贫有着显著的正向影响以及正的空间溢出效应。据此,对未来建设数字乡村提出相关建议,并探索出提升防返贫能力的新路径,未来可尝试从溢出边界进一步探索空间溢出效应。

A Real-coded Genetic Algorithm Applied to Optimum Design of a Low Solidity Vaned Diffuser for Diffuser Pinup

A numerical procedure for hydrodynamic redesign of the conventional vaned diffuser into the low solidity vaned diffuser by means of a real-coded genetic algorithm with Boltzmann, Tournament and Roulette Wheel selection is presented. In the first part, an investigation on the relative efficiency of the different real-coded genetic algorithm is carried out on a typical mathematical test function. The real-coded genetic algorithm with Boltzmann selection shows the best optimization performance compared to the Tournament and Roulette Wheel selection. In the second part, an approach to redesign the vaned diffuser profile is introduced. Goal of the optimum design is to search the highest static pressure recovery coefficient and low solidity vaned diffuser. The result of the low solidity vaned diffuser optimum design confirms that the efficiency and optimization performance of the real-coded Boltzmann selection genetic algorithm outperforms the other selection methods. A comparison between the designed low solidity vaned diffuser and original vaned diffuser shows that the diffuser pump with the redesigned low solidity vaned diffuser has the higher static pressure recovery and improved total hydrodynamic performance. In addition, the smaller outlet diameter of designed vaned diffuser tends to a more compact size of diffuser pump compared to the original diffuser pump. The obtained results also demonstrate the real-coded Boltzmann selection genetic algorithm is a promising optimization algorithm for centrifugal pumps design.

基于改进模糊层次分析法的病险水库除险加固效果评价

为选择可行的加固方案,最大程度地改善病险水库的工作性能,综合考虑除险加固方案的效果可靠性、经济合理性、技术可行性和施工便利性等指标,构建水库除险加固方案多层次优选模型,采用经加速遗传算法改进的模糊层次分析法确定各优选子系统及各指标权重,提出水库加固效果多层次优选评价方法,并选择典型水库进行了验证。结果表明:对于优选子系统,技术可行性和效果可靠性子系统权重较大,分别为0.309和0.298;对于优选指标,综合权重较大的是施工单位水平的高低、稳定性要求满足程度、加固方案与引起大坝加固原因的适应性、加固方案与大坝所处地域适应性等指标。典型水库应用实例中塑性混凝土防渗墙方案综合评价值(0.797)相对较优。

考虑测量不确定性的ANN-Wiener过程加速退化试验评估

考虑加速退化试验过程中关键性能参数的测量不确定性,将测量不确定性处理为区间数,并建立一种结合人工神经网络与Wiener过程的区间加速退化数据可靠性评估方法。基于ANN-Wiener过程构建加速退化数据的负对数似然函数,采用遗传算法建立负对数似然函数未知参数的求解方法,最终实现了区间加速退化试验的可靠性评估。通过激光器的加速退化试验,对该方法进行验证,比较该方法与其他方法评估得到的可靠度和真实可靠度的绝对误差,结果表明,基于ANN-Wiener过程对可靠度的评估结果更准确,且考虑到测量不确定性因素的影响,得到激光器可靠度的保守和乐观估计。相同可靠度的情况下,忽略测量不确定性时的评估时间晚于保守估计的评估时间,会导致产品预防维护时机的推迟,增大产品运行过程中的失效风险,增加因产品失效而造成的损失。

基于加速分层遗传算法-投影寻踪模型的三峡水质评价

针对基于遗传算法的投影寻踪模型的不足,提出了基于加速分层遗传算法的投影寻踪模型,提高了全局搜索能力和收敛速度。并将其应用于长江流域水体水质的评价中。利用长江三峡库区8个断面的水质指标监测数据,建立基于加速分层遗传算法的投影寻踪模型,对各断面水质展开评价。结果显示:该段流域内水体质量整体良好,水质等级主要分布在《地表水环境质量标准》(GB3838-2002)Ⅱ类、Ⅲ类、Ⅳ类等级中。该模型能够合理的评价水质等级,值得广泛推广应用。

Application of the hybrid genetic particle swarm algorithm to design the linear quadratic regulator controller for the accelerator power supply

Purpose The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR.Methods A hybrid genetic particle swarm optimization algorithm is introduced,and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply.The fitness function of hybrid genetic particle swarm optimization is a multi-objective function,which combined the current and voltage,so that the dynamic performance of the closed-loop system can be better.The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R.Results The simulation results show that adoption of this method leads to good transient responses,and the computational time is shorter than in the traditional trial and error methods.Conclusions The results presented in this paper show that the proposed method is robust,efficient and feasible,and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.

基于过度加速度失效模式的船型优化研究

选择第二代完整稳性中过度加速度的长期失效概率为优化目标函数,对船舶进行全船型线优化.采用自由变形技术作为船舶几何重构方法,优化方法采用可避免陷入局部陷阱的随机搜索全局优化算法.对某渔政船进行程序实验,优化后得出的新船型长期失效概率降低64.35%,有效降低了该船发生过度加速度的概率,验证了程序的实用性.

大转动惯量缠绕机加减速曲线优化设计

针对大转动惯量碳纤维缠绕机的加速和减速阶段运行不平稳、传动件易失效破坏等问题,提出了一种基于改进型Sigmoid加减速曲线的主轴运行曲线优化方案。首先,利用五次多项式对传统Sigmoid加减速曲线的跃变处进行补偿,以约束曲线起始点、衔接点、终止点的速度、加速度和急动度(加加速度)。然后,基于改进曲线的速度与加速度函数建立缠绕机的负载扭矩、电机输出功率、主轴强度与刚度以及缠绕圈数的数学模型,并以各阶段运行时长为设计变量、以电机最大输出功率最小和总运行时长最短为优化目标对曲线进行多目标优化,根据缠绕圈数、传动件强度与刚度等的约束条件,利用多目标遗传算法NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ,非支配排序遗传算法-Ⅱ)求解模型的非支配解集,并利用比重选取函数选择最优解。最后,通过AMESim-ADAMS联合仿真对比加减速曲线优化前后缠绕机的运行效果。结果表明:优化后缠绕机的总运行时长、最大加速度、最大负载扭矩和最大输出功率分别降低了41.7%,75.8%,75.5%和72.8%,且主轴的运行曲线更加平滑,验证了优化方案的可行性。研究结果为大转动惯量旋转设备的运行不平稳或传动件失效问题提供了一种新的解决思路。

基于改进遗传算法的消防疏散标识布局优化

消防疏散标识布局对火灾疏散逃生有重要意义,但现有的布局方法存在因布设者经验差异大,地方标准的不同导致标识引导效能差的问题。本文提出了标识引导效能计算思路,构建疏散标识布设优化模型,研究基于自定义算子的改进遗传算法,实现了改进遗传算法的消防疏散标识布局优化方法;并以地铁站台为例开展了实验模拟。结果表明,本文方法能够明显提高消防疏散标识引导效能。这可为室内消防疏散逃生下的标识布设提供科学依据。

一种近红外混合光谱定性模型构建方法

【目的】为了提高模型的通用性,确保在实际的近红外定性模型的构建过程中能够提取近红外光谱更多的有效信息,建立一套稳定的、能够适应外界光谱变化的近红外定性模型。【方法】研究小组采用基于加速遗传算法的投影寻踪方法构建光谱的定性模型。通过混合多批次、多产地、多等级、多部位的光谱作为训练样本集,来提高模型的通用性。然后计算投影特征向量和其他光谱集的投影值,并分别进行比较。【结果】建模集与同批次光谱集的均值的变异系数为0.63%,与不同批次光谱集的均值的变异系数为1.54%,并且,混合不同批次样本前后,不同批次样本集投影值均值的变异系数从1.54%降至0.26%。【结论】通过混合不同批次的光谱,可明显提高模型在不同批次光谱的定性表现。因此,使用投影寻踪方法建立光谱的定性模型具备很好的可行性。

REAL CODED GENETIC ALGORITHM FOR STOCHASTIC HYDROTHERMAL GENERATION SCHEDULING

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.