进化计算理论在结构控制中的应用研究

本文将进化计算理论和方法引入结构控制，首先分析进化计算的概念，原理和方法，然后分析该方法在结构控制中的应用，最后研究进化计算方法中的遗传算法用于设计结构控制中的控制机构的最优布置，以及研究基于遗传算沓的模糊控制器的设计问题。     

计算智能技术在陶瓷材料优化设计中的应用

综述了计算智能在陶瓷材料优化设计中的应用现状,阐明了利用人工神经网络以及遗传算法预测陶瓷材料性能和组分优化的方法,介绍了人工神经网络、遗传算法与免疫算法和模拟退火算法相结合的高效计算智能方法以及模糊神经网络在材料设计中的应用,分析了陶瓷材料优化设计中存在的问题并提出了今后的研究方向。     

基于免疫进化计算的球度误差评定

提出一种新颖的免疫进化计算方法(IEC)用于球度误差评定。该算法基于生物免疫系统的细胞克隆选择学说和生物进化过程中的变异思想构造了自适应变异算子，使系统能够根据环境条件自适应地确定各抗体的变异强度；通过亲和力抑制相似抗体生存并动态地产生新的抗体，以维持抗体种群的多样性。同时将该算法应用于球度误差最小区域评定，并与其它方法进行比较，结果证明该方法具有较强的自适应环境能力，全局收敛性好，提高了球度误差评定精度。     

用遗传算法计算设计多薄层雷达吸波材料的程序实现技术

本研究探索一种集成式多薄层吸波材料的优化设计系统的方法及程序实现技术.该设计系统采用传输线理论和跟踪计算方法来计算多层吸波材料的反射率,以遗传算法作为优化引擎.利用开放源代码数据库服务器软件MYSQL实现了材料电磁参数数据库.在开放源代码软件平台下,创建了多薄层RAMs计算设计和性能预报智能系统.对几种典型的实际材料,进行了多代遗传、进化等优化计算,给出了比较理想的优化设计结果,并对优化结果和材料智能预报系统功能进行了技术评价.     

神经网络的进化学习方法研究

提出了两种用于前向神经网络的进化学习算法,实验证明它们能有效地网络权值空间中寻找全局最优解。在比较实验的基础上,得出了在神经网络的进化学习过程中变异是起主导作用的遗传算子的结论,并以此为指导配置算法的各个关键参数。通过对ＸＯＲ问题和ＩＲＩＳ模式分类问题的学习证明,这两种算法均能获得远高于传统的ＢＰ算法的性能。     

基于Matlab的管壳式换热器分段计算研究

以折流板结构为基础将管壳式换热器分成一定的计算单元,通过对每个分段单元的研究,从流体物性沿壳体方向变化方面来对管壳式换热器进行整体的工艺设计及计算。并且基于微分法,提出了一种较为准确预测出管壳式换热器管、壳侧壁温分布的方法。通过工程案例分析,并将壁温计算结果与HTRI进行对比得出,管、壳程各单元的壁温平均误差分别1.76%和0.94%,该结果也验证了壁温计算方法的可行性。对于传热系数与压降,与 HTRI 对比得出：变物性计算较定物性在精度有一定的提高。同时通过借助Matlab计算机软件编制了一套用于管壳式换热器分段计算及优化设计程序,实现了换热器的精确优化设计的同时也提高了计算效率,变物性的研究理论为高效低能耗换热器的设计提供了思路和方法,具有一定的工程应用价值。     

水平管降膜式蒸发器的优化设计模型

对降膜式蒸发器进行热力分析建模,提供一种性能优化的数值计算方法,在模型中引入成本估算算法,用于对降膜式蒸发器的优化设计提供指导。并用样机试验结果验证该计算模型的准确性,为工程设计提供参考。     

基于复合形算子的基础支护桩优化设计智能算法研究

本文通过遗传算法和传统复合形搜索法相结合,基于对遗传算法算子计算结构的调整,并将遗传算法与神经网络相结合,提出并研究了一种新的优化设计方法,协同求解复杂工程中的优化问题。并针对悬臂式支护桩的优化设计的数学模型,采用该算法进行了优化设计分析;计算结果表明,该算法可克服遗传算法最终进化至最优解较慢和人工神经网络易陷入局部解的缺陷,具有较好的全局性和收敛速度。     

坛紫菜5.8S rDNA和ITS区片段的序列分析及应用

对坛紫菜(Porphyra haitanensis)野生(GL)和栽培(PXV)品系的5.8S rDNA-ITS兀区进行了PCR扩增和序列分析,扩增的GL和PXV的DNA片段长度分别为1213 bp和1221bp,包含完整的ITS1-5.8S-ITS2区.然后对紫菜7个种9个品系(其中6种7个品系从GenBank数据库中获得)的rDNA相应序列进行了排序和系统进化分析,结果表明:9个紫菜品系rDNA中5.8S区的长度和序列非常保守,而ITS区的长度和序列则变异较大;根据它们的序列差异,计算出这9个紫菜品系的遗传距离在0.010～0.551之间,遗传相似性在44.9%～99%之间;并且采用邻接法构建了这9个紫菜品系的系统发育树,发现可以明显分为4个进化枝,由此讨论了分子分类方法同传统分类方法的分歧.实验结果表明,5.8S rDNA.ITS区序列可以成为紫菜种质鉴定和系统进化研究的强有力工具.     

进化计算在生产调度问题中的应用

本文首先简要介绍了进化计算方法的基本思想和特点,然后对遗传算法求解度问题的某些策略和基本步骤作了简要的归纳和总结,并且初步提出了应用进化策略和进化规划求解调度问题的一般方法。     

Integrated model for the batch sequencing problem in a multi-stage supply chain: an artificial immune system based approach

In this paper a mathematical model for the batch sequencing problem in a multistage supply chain is developed by taking into account three practically important objectives, viz. minimization of lead time, blocking time and due date violation. Attribute dependent operation time, sequence dependent setup time, different due dates, different lot sizes for batches and variable time losses due to interaction among several stages like waiting, idling, and blocking are also considered in the model. The problem is combinatorial in nature and complete enumeration of all its possibilities is computationally prohibitive. Therefore, a metaheuristic, artificial immune system (AIS) is employed to find an optimal/near optimal solution. In order to test the efficacy of AIS in solving the problem, its implementation on four different problems has been studied. Further, the comparative analysis of the results obtained by implementing AIS, genetic algorithm (GA) and simulated annealing (SA) on the proposed model reveals that AIS outperforms GA and SA in solving the underlying problem.     

Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.     

Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms

Energy consumption of a rail transit system depends on many parameters. One of the most effective methods of reducing energy consumption in a rail transit system is optimising the speed profile of the trains along the route. A new efficient method will be presented for the optimisation of the coasting points for trains in a global manner. The proposed approach includes realistic system modelling using multi-train, multi-line simulation software and application of artificial neural networks (ANN) and genetic algorithms (GA). The simulation software used can model regenerative braking and train performance at low voltages. Using ANN and GA together, optimal coasting points for long line sections covering five stations and two lines are achieved. Simulation software is used for creating training and test data for the ANN. These data are used for training of the ANN. Trained ANNs are then used for estimating energy consumption and travel time for new sets of coasting points. Finally, the outputs of the ANN are optimised to find optimal train coasting points. For this purpose, a fitness function with target travel time, energy consumption and weighting factors is proposed. An interesting observation is that the use of ANN increases the speed of optimisation. The proposed method is used for optimising coasting points for minimum energy consumption for a given travel time on the first 5 km section of Istanbul Aksaray-Airport metro line, where trains operate every 150 s. The section covers five passenger stations, which means four coasting points for each line. It has been demonstrated that an eight input ANNs can be trained with acceptable error margins for such a system.     

A hybrid genetic-immune algorithm with improved lifespan and elite antigen for flow-shop scheduling problems

In this paper, a hybrid genetic-immune algorithm (HGIA) is proposed to reduce the premature convergence problem in a genetic algorithm (GA) in solving permutation flow-shop scheduling problems. A co-evolutionary strategy is proposed for efficient combination of GA and an artificial immune system (AIS). First, the GA is adopted to generate antigens with better fitness, and then the population in the last generation is transformed into antibodies in AIS. A new formula for calculating the lifespan of each antibody is employed during the evolution processes. In addition, a new mechanism including T-cell and B-cell generation procedures is applied to produce different types of antibodies which will be merged together. The antibodies with longer lifespan will survive and enter the next generation. This co-evolutionary strategy is very effective since chromosomes and antibodies will be transformed and evolved dynamically. The intensive experimental results show the effectiveness of the HGIA approach. The hybrid algorithm can be further extended to solve different combinatorial problems.     

Using GA–ANN algorithm to optimize soft magnetic properties of nanocrystalline mechanically alloyed Fe–Si powders

In this investigation a theoretical model based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to optimize the magnetic softness in nanocrystalline Fe–Si powders prepared by mechanical alloying (MA). The ANN model was used to correlate the milling time, chemical composition, milling speed, and ball to powders ratio (BPR) to coercivity and crystallite size of nanocrystalline Fe–Si powders. The GA–ANN combined algorithm was incorporated to find the optimal conditions for achieving the minimum coercivity. By comparing the predicted values with the experimental data it is demonstrated that the combined GA–ANN algorithm is a useful, efficient and strong method to find the optimal milling conditions and chemical composition for producing nanocrystalline Fe–Si powders with minimum coercivity.     

Pneumatic motor speed control by trajectory tracking fuzzy logic controller

In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions (MF) and weights of control rules. In addition, artificial neural networks (ANN) modelled dynamic behaviour of PM is given. This ANN model is used to find the optimal TTFLC parameters by offline GA approach. The experimental results show that designed TTFLC successfully enables the PM speed track the given trajectory under various working conditions. The proposed approach is superior to PID controller. It also provides simple and easy design procedure for the PM speed control problem.     

遗传神经网络在木塑复合材料力学性能预测中的应用

利用遗传算法(GA)和神经网络对木塑复合材料力学性能进行预测。首先利用神经网络构建木塑复合板材主要工艺参数热压时间(T)、马来酸酐(MA)和废旧塑料聚丙烯(PP)与材料力学性能内结合强度(IB)、静曲强度(MOR)、弹性模量(MOE)和吸收厚度膨胀率(TS)之间的关系模型,然后利用遗传算法对模型进行优化和训练;最后利用训练好模型对材料的力学性能进行预测以及模型验证。结合显示优化模型预测的板材的MOE的误差范围分别为2%～15.5%、9%～38%和4%～70%,远小于未优化模型的预测误差8%～1491%、2.8%～1950%和15%～128%;对IB、MOR和TS的预测也有相似的结果。     

Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don’t have acceptable accuracy.     

Integrated ANN–GA for estimating the minimum value for machining performance

In this study, we proposed a new approach in estimating a minimum value of machining performance. In this approach, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated in order to search for a set of optimal cutting condition points that leads to the minimum value of machining performance. Three machining cutting conditions for end milling operation that were considered in this study are speed (v), feed (f) and radial rake angle (γ). The considered machining performance is surface roughness (R _a). The minimum R _a value at the optimal v, f and γ points was expected from this approach. Using the proposed approach, named integrated ANN–GA, this study has proven that R _a can be estimated to be 0.139?µm, at the optimal cutting conditions of f?=?167.029?m/min, v?=?0.025?mm/tooth and γ?=?14.769°. Consequently, the ANN–GA integration system has reduced the R _a value at about 26.8%, 25.7%, 26.1% and 49.8%, compared to the experimental, regression, ANN and response surface method results, respectively. Compared to the conventional GA result, it was also found that integrated ANN–GA reduced the mean R _a value and the number of iterations in searching for the optimal result at about 0.61% and 23.9%, respectively.     

Optimization of chemical composition for TC11 titanium alloy based on artificial neural network and genetic algorithm

It is quite difficult for materials to develop the quantitative model of chemical elements and mechanical properties, because the relationship between them presents the multivariable and non-linear. In this work, the combined approach of artificial neural network (ANN) and genetic algorithm (GA) was employed to synthesize the optimum chemical composition for satisfying mechanical properties for TC11 titanium alloy based on the large amount of experimental data. The chemical elements (Al, Mo, Zr, Si, Fe, C, O, N and H) were chosen as input parameters of the ANN model, and the output parameters are mechanical properties, including ultimate tensile strength, yield strength, elongation and reduction of area. The fitness function for GA was obtained from trained ANN model. It is found that the percentage errors between experimental and predicted are all within 5%, which suggested that the ANN model has excellent generalization capability. The results strongly indicated that the proposed optimization model offers an optimal chemical composition for TC11 titanium alloy, which implies it is a novel and effective approach for optimizing materials chemical composition.