A hybrid genetic-simulated annealing algorithm for optimization of hydraulic manifold blocks

This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks （HMB）. Due to the limitation of its local search ability of genetic algorithm （GA） in solving a massive combinatorial optimization problem, simulated annealing （SA） is combined, the multi-parameter concatenated coding is adopted, and the memory function is added. Thus a hybrid genetic-simulated annealing with memory function is formed. Examples show that the modified algorithm can improve the local search ability in the solution space, and the solution quality.

Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks

Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model.

Optimal design of pressure vessel using an improved genetic algorithm

As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.

An Optimization Design of a Weft Insertion Mechanism for Rapier Looms

By analyzing a combined and spatial 6-bar linkage weft insertion mechanism, its practical model for optimization design is set up and the modification of penalty strategy is put forward so that the genetic algorithm can be better used in optimization design for mechanisms with non- linear constraints. The design result is discussed.

Optimal Building Frame Column Design Based on the Genetic Algorithm

Building structure is like the skeleton of the building,it bears the effects of various forces and forms a supporting system,which is the material basis on which the building depends.Hence building structure design is a vital part in architecture design,architects often explore novel applications of their technologies for building structure innovation.However,such searches relied on experiences,expertise or gut feeling.In this paper,a new design method for the optimal building frame column design based on the genetic algorithm is proposed.First of all,in order to construct the optimal model of the building frame column,building units are divided into three categories in general:building bottom,main building and building roof.Secondly,the genetic algorithm is introduced to optimize the building frame column.In the meantime,a PGA-Skeleton based concurrent genetic algorithm design plan is proposed to improve the optimization efficiency of the genetic algorithm.Finally,effectiveness of the mentioned algorithm is verified through the simulation experiment.

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

Robust design and optimization for autonomous PV-wind hybrid power systems

This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.

Control parameter optimal tuning method based on annealing-genetic algorithm for complex electromechanical system

A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that AGA takes objective function as adaptability function directly,so it cuts down some unnecessary time expense because of float-point calculation of function conversion.The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented.It can be applied to a wide class of problems.The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA.The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.

Design optimization of transonic compressor stage using CFD and response surface model

In order to shorten the design period, the paper describes a new optimization strategy for computationally expensive design optimization of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), genetic algorithm (GA) and a 3-D Navier-Stokes solver(Numeca Fine). Data points for response evaluations were selected by improved distributed hypercube sampling (IHS) and the 3-D Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. To maximize the adiabatic efficiency, the genetic algorithm was applied to the response surface model to perform global optimization to achieve the optimum design of NASA Stage 35. An optimum leading edge line was found, which produced a new 3-D rotor blade combined with sweep and lean, and a new stator one with skew. It is concluded that the proposed strategy can provide a reliable method for design optimization of turbomachinery blades at reasonable computing cost.

基于高维混合模型的离心泵叶轮子午面优化设计

为提高离心泵在设计工况下的运行效率和扬程,提出一种基于高维混合模型的离心泵叶轮优化设计方法.选取一台比转数为157的单级离心泵作为研究对象,通过CFturbo软件对优化变量进行参数化,然后结合数值模拟获得高维混合模型的训练集.在此基础上采用获取的训练集通过MATLAB机器学习得出效率、扬程与优化参数之间关于支持向量回归的高维模型,并采用遗传算法寻优.在设计工况下,所拟合的高维混合模型预测的效率和扬程值比原模型分别高1.5%和3.2 m,数值模拟验证优化方案的效率和扬程分别比原模型高0.9%和2.1 m.算例研究表明,将高维混合模型应用于离心泵叶轮的优化设计中可以实现快速寻优并提高离心泵水力性能.

基于遗传算法优化的潜液泵效率提升研究

为了提高潜液泵效率并解决潜液泵多部件多参数优化的难题,提出一种基于遗传算法优化的潜液泵效率提升优化设计方法。选取潜液泵效率作为优化的目标函数,以叶轮和诱导轮作为优化的核心部件,以潜液泵叶轮进口角β_(1)、叶轮出口角β_(2)、叶片包角ϕ、叶片进口宽度b_(2)、诱导轮进口角β_(3)、诱导轮出口角β_(4)、诱导轮叶片倾角γ作为约束条件,利用遗传算法进行优化,并利用ANSYS Fluent软件进行数值模拟验证。数值模拟结果表明:综合考虑叶轮与诱导轮优化的潜液泵与原泵相比,其工作效率由原来的71.65%提升至80.40%,且增压效果更明显,并且效率提升效果比单一优化叶轮或诱导轮的泵更好。通过遗传算法对LNG潜液泵的叶轮以及诱导轮进行综合优化,显著提高了泵的工作效率。

纳弧度级柔性角位移调节机构的优化设计

针对同步辐射中纳弧度级角位移调节的需求,优化设计并研制出一套冗余并联式柔性铰链转动装置。分析了柔性机构的运动学机理,利用虚位移原理推导出机构的整体静态转动刚度,研究其特性及铰链各参数对其影响。根据拉格朗日方程建立其动力学模型,推导出机构在运动方向的固有频率。建立数学优化模型,进行了机构静态和动态的双目标优化设计,采用基因遗传算法对带有非线性约束条件的目标函数优化求解。利用有限元方法对优化后的机构进行了模态分析,研究了柔性机构的前四阶固有频率和振型。制作出高精度的柔性铰链机构,设计搭建转动调节装置进行实验测试。测试结果表明:柔性角位移调节机构转角可达到0.668°,微调时双向运动重复精度实现±8.91 nrad,角分辨率为15 nrad,1~500 Hz的30 min稳定性(均方根值)为2.72 nrad,机构的一阶固有频率约295 Hz,与理论计算和有限元分析相一致。结果验证了优化设计的柔性机构实现纳弧度级高精度角位移调节的有效性和可靠性。

干气中冷油闪蒸工艺模拟与多目标优化

针对干气提浓装置能耗较高的现状,对最新的中冷油闪蒸工艺进行了研究。采用Aspen Plus软件进行流程模拟,使用改进的遗传算法(NSGA-Ⅱ),以年总费用(TAC)､CO_(2)排放量(E_(carbon))和碳二回收率(R_(C_(2)))为目标函数,通过罚函数法转化为无约束问题,对中冷油闪蒸工艺进行多目标优化,获得了Pareto前沿。统计后发现,半贫液与贫液质量比的变异系数仅为2.37%,可以使用平均值1.95来代表。最后使用优劣解距离法(TOPSIS)选取最优点进行对比,优化结果显示,相比于浅冷油吸收工艺,中冷油闪蒸工艺的R_(C_(2))上升3.09%,TAC下降43.75%,E_(carbon)减少41.77%。结果表明,中冷油闪蒸工艺在各方面性能均有大幅提升,且基于NSGA-Ⅱ算法的多目标优化方法能够发现更多的有益性结论。

基于遗传算法的海洋脐带缆截面布局优化设计与数值验证

海洋脐带缆通常由不同的功能构件捆绑而成,而这些功能构件的力学性能差异很大,在外载荷的作用下,不合理的截面布局可能会导致较大的截面变形和构件间的接触压力,从而影响脐带缆服役寿命。本文首先利用最小化截面半径给出截面布局紧凑性的实现方法,通过基于截面构件的拉伸刚度引入虚拟重力指标来描述截面布局的对称性,同时提出可量化的指标描述易损构件钢管之间的疲劳磨损问题。然后考虑上述三个目标建立截面布局多目标优化模型,并引入遗传算法对上述模型进行求解优化,自动得到三种具有代表性的截面优化布局。最后,通过数值模拟对不同截面优化布局进行验证与分析评价,进而得到最优截面布局设计。本文所提出的脐带缆截面布局设计优化方法可提高全局最优解搜索能力,对脐带缆结构设计具有一定的指导意义。

基于多岛遗传算法的大流量离心泵水力性能优化研究

离心泵的效率和汽蚀性能是关系到离心泵能效和稳定性的关键指标,本文以Isight多学科优化平台为基础,将参数化建模、网格划分、数值计算、优化分析等水力设计流程有机结合,构建了离心泵自动优化设计平台。以效率指标和汽蚀余量为目标函数,采用多岛遗传算法对比转速为202m, m^(3)/s的大流量离心泵进行了多目标性能优化。结果表明:多岛遗传算法能够有效提升大流量离心泵水力性能,其主要表现为离心泵设计效率提高0.4%,小流量工况效率整体提高2%~3%,设计工况汽蚀余量降低3m。

扩压式自泵送流体动压机械密封性能分析及双目标优化研究

泄漏率和开启力是在研究非接触式机械密封性能需要考虑的2个重要指标.目前针对非接触式机械密封的结构优化研究,仅限于固定内径的密封环,优化结果缺乏通用性.以新型扩压式自泵送流体动压机械密封为研究对象,利用数值模拟方法分析了端面重要结构参数对其密封性能的交互影响;基于均匀试验设计和多元回归分析法建立了扩压式自泵送流体动压机械密封的开启力和泄漏率预测模型;采用NSGA2遗传算法进行双目标寻优,得到Pareto最优解集,再结合TOPSIS法从解集中筛选出了不同权重下的最优结构参数.研究结果表明:对于中小轴径的扩压式自泵送流体动压机械密封,动环端面密封坝坝长与扩压环槽槽宽的比值在1.46~2.01之间,螺旋槽槽长和扩压环槽槽宽的比值在1.32~1.86之间时,密封性能最优.研究成果为扩压式自泵送流体动压机械密封动环端面的设计提供理论基础.

基于改进模拟退火遗传算法的路径规划问题研究

为了解决复杂环境中的路径规划问题,通过引入并改进模拟退火算法与遗传算法相结合的混合优化策略,以克服传统路径规划算法在全局搜索能力、收敛速度及避免局部最优解方面的局限性,提出了一种基于改进模拟退火遗传算法的路径规划方法。在遗传算法框架内,通过编码方式表示路径,并利用选择、交叉和变异等遗传操作生成新的路径种群。为增强全局搜索能力和跳出局部最优解的能力,引入了模拟退火机制,在遗传算法的交叉和变异操作中融入模拟退火的概率接受准则,允许以一定概率接受较差的解,从而增加种群的多样性。研究过程中,首先设计并实现了改进的模拟退火遗传算法,并设置了对比实验,包括单独使用遗传算法、模拟退火算法以及模拟退火遗传算法进行对比分析。实验结果表明,与单独使用遗传算法和模拟退火算法相比,改进模拟退火遗传算法在复杂环境中的路径规划问题上展现出了显著的优势,有效提升了算法的全局搜索能力、最优解准确度和收敛速度,同时增强了算法对复杂环境的适应能力。

Optimization of Clinching Tools by Integrated Finite Element Model and Genetic Algorithm Approach

Clinching is a convenient and efficient cold forming process that can join two sheets without any additional part. This study establishes an intelligent system for optimizing the clinched joint. Firstly, a mathematical model which introduces the ductile damage constraint to prevent cracking during clinching process is proposed.Meanwhile, an optimization methodology and its corresponding computer program are developed by integrated finite element model(FEM) and genetic algorithm(GA) approach. Secondly, Al6061-T4 alloy sheets with a thickness of 1.4 mm are used to verify this optimization system. The optimization program automatically acquires the largest axial strength which is approximately equal to 872 N. Finally, sensitivity analysis is implemented, in which the influence of geometrical parameters of clinching tools on final joint strength is analyzed. The sensitivity analysis indicates the main parameters to influence joint strength, which is essential from an industrial point of view.

基于多岛遗传算法的电动拖拉机分布式驱动系统优化设计与试验

针对分布式驱动电动拖拉机(Distributed drive electric tractor, DDET)牵引效率低、系统能量损耗大的问题,提出了一种基于多岛遗传算法(Multi-island genetic algorithm, MIGA)的分布式驱动系统参数优化设计与验证方法。根据犁耕作业工况,建立了拖拉机分布式驱动系统7自由度耦合动力学模型以及轮胎-土壤交互模型,完成了驱动系统关键部件参数设计和匹配选型。提出基于MIGA的前后轮边传动比参数优化策略,将轮边传动比作为决策变量,驱动系统能量损失最小为优化目标,驱动电机功率和转速为约束条件。搭建Matlab/Simulink-NI PXI联合仿真平台验证了参数优化策略的正确性和实时可执行性。结果表明,基于MIGA参数优化后的分布式驱动系统各方面性能得到了有效提升。犁耕循环工况下,拖拉机平均牵引力为10 610 N,最大牵引功率为31.25 kW;平均效率提升了0.38%,驱动电机能耗降低了7.53%。本研究可为分布式驱动电动拖拉机优化设计和系统控制提供理论基础和验证方法。

基于遗传算法的RV减速器生产线优化设计

精密RV减速器是发展工业机器人技术的关键一环,但是目前国内仍很难对其实现规模化生产。文章针对RV减速器混流装配线存在的生产不平衡和整体效率低的问题建立数学模型,提出通过考虑紧前工序的交换变异遗传算法来解决传统遗传算法易陷入最优解的缺陷,然后采用Flexsim软件对RV减速器混流装配生产线进行建模和仿真,验证了所提方法的有效性。验证结果表明,优化后生产节拍缩短了0.8,生产平衡率提升了18%,而平滑度指数从1.3下降至0.72,生产效率得到显著提高。