序列数编码的遗传算法柔性优化多股流板翅式换热器通道排列

实数编码的遗传算法在优化多股流板翅式换热器通道排列中进行个体间交叉、变异操作后,存在无法保证各流体的通道数恒定不变的问题。对此,以通道的热负荷累积均方差为目标函数,建立通道排列的优化模型,提出基于序列数编码方式的改进遗传算法进行多股流板翅式换热器通道优化设计,实现了个体间交叉和变异遗传操作。对一包含4个流股57个通道的板翅式换热器通道排列进行优化,并通过多工况点设计对系统柔性进行分析。结果表明:本文优化设计方案的累积热负荷均方差为3562.9W,比文献经验值小2.7%,相比于实数编码遗传算法得到的结果,减少了15.1%,且累积热负荷在零线上下均匀波动。文章表明序列数编码的遗传算法增加了遗传算法种群中个体的多样性,提高了搜索效率具有更好的全局搜索能力。     

一种新的DNA遗传算法及其在参数估计中的应用

化工过程的参数估计是十分棘手的问题,为此常将这类问题转化为非线性优化问题来解决。遗传算法是一种适应性强的全局搜索方法,常被用于解决非线性系统的参数估计问题。但其局部搜索能力较差,易早熟。针对遗传算法的缺点,提出了一种新的DNA遗传算法。该方法使用碱基对个体进行四进制编码,受DNA分子操作启发设计了新的交叉和变异算子。两个经典测试函数的计算结果表明,该算法的搜索能力相对于其他两种算法有了明显提高。使用该算法来估计重油热解三集总模型中的参数,结果表明所建模型拟合精度高。     

基于插值评价的遗传算法及其在参数估计中的应用

针对基于实数编码的遗传算法 ,引入相似度和可信度的概念 ,采用基于插值的方法对适配值进行评价 ,进而减少评价环节的计算量 ,提高整个算法的效率和实时性。通过对系统参数估计问题的求解 ,验证了所提方法的可行性和有效性     

基于混合神经网络与UG/KF方法的浇口位置优化设计

在分析遗传算法特点的基础上,针对遗传算法优化搜索过程中收敛速度慢和不成熟收敛的缺点,提出一种改进的实数编码混合遗传算法,借助于知识熔接技术即KF语言进行几何建模,建立基于混合神经网络与UG/KF方法的浇口位置优化设计,利用CAE软件进行模拟,获取训练样本,用Matlab语言编制应用程序,运用参数优化系统对浇口的位置进行优化计算,结合UG/KF,实现知识驱动.     

基于遗传算法的汽车永磁发电机多目标优化设计

提出了一种基于遗传算法的汽车永磁发电机多目标优化设计。该设计通过改进的遗传算法进行多目标优化。改进的遗传算法采用二进制编码,规范化几何秩选择,混合交叉及均匀变异。优化结果表明,在单目标函数和多目标函数情形下均搜索到全局最优解,遗传算法为多目标优化提供了有效的途径。同时经过反复计算后,获得一些具有实用价值的经验数据,对永磁电机的设计起到一定指导作用。     

应用遗传算法实现精馏分离序列优化综合

阐述了采用遗传算法求解具有组合爆炸特征的精馏分离序列综合问题。首先在数据结构上将精馏分离序列抽象为二叉树结构编码，然后应用图论方法建立起有效的交叉和变异算子，最后对10组分精馏分离序列综合问题实例进行求解。结果表明，遗传算法对最优解命中率达80％，平均空间搜索率仅为0．0318。因此遗传算法能够成功解算大规模精馏分离序列优化综合问题。     

带修复操作整型编码遗传算法求解大规模机组组合问题

针对发电机组组合调度问题,提出了一种带修复操作的整型编码遗传算法(r-ICGA)。算法采用整数串的编码方式,有效减小了染色体的长度。同时引入一组新的修复操作来处理约束,将进化过程中产生的新个体修复成为可行个体。与罚函数约束处理方法相比,所提算法不引入惩罚项,避免了针对不可行解的经济负载分配子问题求解,节省了大量计算时间。将所提方法应用于六种不同规模的机组组合问题,仿真结果表明算法的搜索效率更高,求得的调度结果更好。随机组规模增大,算法所需执行时间近似线性地平缓增长,表明r-ICGA算法比其他方法更适合于求解大规模机组组合调度问题。     

遗传算法在催化体系的全局结构优化中的应用

对催化体系进行全局结构优化,搜寻基态结构对预测催化剂结构、分析反应物的吸附特性、研究多相催化反应机理、构建实际反应路径等方面至关重要。遗传算法通过交叉、变异和选择等操作,模拟了自然淘汰进化过程,来搜索势能面上的基态结构。作为一种无偏优化算法,遗传算法的优化过程不依赖于输入结构,具有很强的全局搜索能力。对遗传算法在催化体系的全局结构优化问题中的应用进行了综述,介绍了遗传算法在实空间上进行全局结构优化的基本程序框架以及近年来结合并行计算、机器学习等技术发展的改进框架,并讨论了它们在团簇优化、负载型催化剂的结构优化问题上的相关应用,为遗传算法的进一步改进以及更广泛的应用提供理论指导。     

基于改进自适应遗传算法的复合材料铺层优化设计

为提高采用遗传算法的复合材料铺层优化计算效率,提出了改进的自适应遗传算法,其交叉和变异算子在迭代过程中能够根据种群迭代收敛趋势进行自适应调整。通过二维Rasrtigrin测试函数试验,表明改进自适应遗传算法能够增加全局收敛次数,提高收敛速度。在此基础上,编写整数编码的改进自适应遗传算法,并结合经典层合板理论,以质量轻量化为目标,结构强度和刚度为约束条件,对复合材料层合板进行铺层优化设计,算例迭代过程显示收敛到最优解自适应遗传算法需进化27代,改进自适应遗传算法需进化19代。结果表明,提出的改进自适应遗传算法能够明显提高复合材料铺层优化设计效率。     

基于自适应遗传算法的低噪声轮胎花纹结构参数优化

在遗传算法的基础上引入自适应概念,提出自适应遗传算法,并用于低噪声轮胎花纹结构参数优化。自适应遗传算法有效克服了遗传算法易早熟收敛、搜索效率低等缺陷,提高了优化速度和效率,减少了收敛代数,软件稳定性改善。     

混沌遗传算法估计反应动力学参数

为了准确地估计反应动力学参数 ,提出一种混沌遗传算法 (chaosgeneticalgorithm ,CGA) ,它基于混沌变量的遗传操作 ,将使子代个体均匀地分布于定义空间 ,从而可避免早熟 ,以较大的概率实现全局最优搜索 .与传统的遗传算法相比较 ,CGA的在线和离线性能都有较大的改进 .将CGA应用于 2 -氯苯酚在超临界水中氧化反应动力学参数的估算 ,获得了满意的结果     

基于EGA的RBF-PLS方法为脉冲萃取过程建模

针对径向基网络(RBFN)结构与参数难以确定的问题,在分析径向基函数-偏最小二乘(RBF-PLS)方法的基础上,提出以模型拟合和预报性能为目标,同时优选RBF的宽度参数和PLS成分数,并设计了基于优进策略的遗传算法(EGA)实施优化。EGA增加了模式搜索寻优算子,对交叉算子作了改进,自适应地调整交叉率和变异率,由此形成EGA-RBF-PLS方法,并将它应用于回收ε-己内酰胺的脉冲萃取过程。它工作量小,效果良好,所建模型的拟合和预报性能明显优于近似机理模型和其它RBFN模型,稳健性也更好。     

基于模糊自适应遗传算法的数据校正方法

针对数据校正中的非线性数据分类还比较困难的问题,提出表上作业法,结合遗传算法同时进行数据协调与过失误差侦破.并将模糊数据协调模型中的三角形约束引入遗传算法界定其变异上下限,还对交叉算子进行自适应改进,从而形成了基于模糊自适应的数据校正方法并用该方法对一个稳态多组分精馏过程进行大量仿真试验,结果表明了算法的有效性.     

基于一种改进的自适应遗传算法估算环已烷自催化氧化反应动力学参数

A modified genetic algorithm of multiple selection strategies, crossover strategies and adaptive operator is constructed, and it is used to estimate the kinetic parameters in autocatalytic oxidation of cyclohexane. The influences of selection strategy, crossover strategy and mutation strategy on algorithm performance are discussed. This algorithm with a specially designed adaptive operator avoids the problem of local optimum usually associated with using standard genetic algorithm and simplex method. The kinetic parameters obtained from the modified genetic algorithm are credible and the calculation results using these parameters agree well with experimental data. Furthermore, a new kinetic model of cyclohexane autocatalytic oxidation is established and the kinetic parameters are estimated by using the modified genetic algorithm.     

模糊自适应遗传算法及其在化工中的应用

Considering that the performance of a genetic algorithm (GA) is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.     

Optimization of a Reverse Osmosis System Using Genetic Algorithm

Abstract

Reverse Osmosis (RO) has found extensive application in industry as a highly efficient separation process. In most cases, it is required to select the optimum set of operating variables such that the performance of the system is maximized. In this work, an attempt has been made to optimize the performance of RO system with a cellulose acetate membrane to separate NaCl‐Water system using Genetic Algorithm (GA). The GAs are faster and more efficient than conventional gradient based optimization techniques. The optimization problem was to maximize the observed rejection of the solute by varying the feed flowrate and overall permeate flux across the membrane for a constant feed concentration. To model the system, a well‐established transport model for RO system, the Spiegler‐Kedem model was used. It was found that the GA converged rapidly to the optimal solution at the 8th generation. The effect of varying GA parameters like size of population, crossover probability, and mutation probability on the result was also studied. The algorithm converged to the optimum solution set at the 8th generation. It was also seen that varying the computational parameters significantly affected the results.     

A hybrid DNA based genetic algorithm for parameter estimation of dynamic systems

Inspired by the evolutionary strategy and the biological DNA mechanism, a hybrid DNA based genetic algorithm (HDNA-GA) with the population update operation and the adaptive parameter scope operation is proposed for solving parameter estimation problems of dynamic systems. The HDNA-GA adopts the nucleotides based coding and some molecular operations. In HDNA-GA, three new crossover operators, replacement operator, transposition operator and reconstruction operator, are designed to improve the population diversity, and the mutation operator with adaptive mutation probability is applied to guarantee against stalling at local peak. Besides, the simulated annealing based selection operator is used to guide the evolution direction. In order to overcome the premature convergence drawbacks of GAs and enhance the algorithm global and local search abilities, the population update operator and the adaptive parameter scope operator are suggested. Numerous comparative experiments on benchmark functions and real-world parameter estimation problems in dynamic systems are conducted and the results demonstrate the effectiveness and efficiency of the HDNA-GA.     

Cloud Point Extraction of Glycyrrhizic Acid from Licorice Root

Abstract

An attempt has been made to extract glycyrrhizic acid (GA) from licorice root by surfactant mediated cloud point extraction (CPE) using non‐ionic surfactant (Triton X‐100). Almost all of the GA molecules were concentrated in the surfactant‐rich phase (also called coacervate phase) after phase separation. The pH is the most critical factor regulating the distribution of GA in the micelle which related to the ionization form. The other effects of the concentration of GA and the surfactant, the temperature, and the salt concentration on the extraction efficiency of GA in the coacervate phase and aqueous phase have been studied. The mechanism of CPE of GA was explored with transmitting electron microscopy. It was deduced that aggregate GA molecules were adsorbed on micelles' outer poler mantle and inner cross‐linked micelles at high GA concentrations in coacervate phase.