一种综合改进型遗传算法及其在多变量解耦中的应用

针对传统的遗传算法存在的未成熟收敛问题,提出一种综合改进型遗传算法,通过对选择算子、交叉算子、变异算子等方面进行综合设计,能有效改善遗传算法的全局搜索性能.基于该算法提出了一种复杂工业对象多变量系统解耦控制器的设计方法,对实际工业对象的解耦控制器设计结果表明该算法是一种通用高效率的最优化搜索方法.     

基于改进遗传算法的二自由度PID控制注射机液压控制系统

基于二自由度PID控制器的注射机液压控制系统,采用改进遗传算法进行二自由度参数整定。半实物仿真实验表明:改进遗传算法通过优化选择算子和变异算子提高算法搜索速度并保证全局最优性,具有更快的搜索速度和全局最优性,在最优参数的控制下液压系统具有理想的控制效果。     

结合遗传算子的改进粒子群算法在控制系统设计中的应用

针对粒子群优化算法容易陷入局部最优以及早熟等缺点,结合遗传算法的选择交叉变异算子进行改进,得到一种新型PSO算法.将该方法应用于PID控制系统参数调优和被控对象参数辨识,仿真结果显示所提出的算法优化效果优于基本粒子群优化算法和遗传算法,收敛性能也得到较大提高.     

基于遗传算法的自适应最优阈值图像分割技术研究

本文通过对遗传算法搜索过程的改进与优化,采用了变异概率随适应度自动改变的自适应变异方法,提出了一种改进的基于遗传算法的自适应最优阈值图像分割算法。实验结果表明,该算法的收敛性能与分割效果都优于标准遗传算法。     

基于HGA的经费分配多目标优化模型求解

为探索新的经费分配方法和管理模式,建立一种新的多目标非线性规划优化模型,提出一种先进的基于正交试验的新型混合遗传算法来求解该问题。对求解过程中的选择算子、交叉算子和变异算子等进行正交试验,得到的种群个体明显优于基本遗传算法的个体,仿真结果表明,该算法收敛寻优能力强,并能产生很多次优解,是一种高效的方法。     

遗传算法在过程综合混合整数非线性规划中的应用

针对过程综合混合整数非线性规划问题提出了一种混合遗传算法，该算法对整数变量进行编码，而对连续变量则采用连续化遗传算子进行处理，使得算法与原问题的对应更加自然，也更有效。另外，还针对常见的提前收敛或局部最小现象提出几种算子，实例表明本算法具有较强的全局优化能力。相对于传统遗传算法具有速度快、精度高、可操作性强等特点。     

基于遗传算法的分子设计初探

本文介绍了一种基于遗传算法思想的计算机辅助分子设计（CAMD）方法，该法以DUNIFAC法基团形式进行基因编码，并对基础遗传算法中的选择，杂交和变异等算子进行了修正，同时提出了倒身和单性两个新的操作算子，最后用实例说明该方法的有效性。     

改进生物地理学优化算法及其在汽油调合调度中的应用

汽油调合和调度优化问题中含有典型的非线性约束(NLP)问题。针对一般智能优化算法在解决此类优化问题中易陷于局部极值,提出了一种改进的生物地理学优化算法(HMBBO)。该算法设计了一种基于种群个体差异信息的启发式变异算子,弥补了Gauss变异、Cauchy变异算子缺乏启发式信息的不足,以解决原算法在局部搜索时易出现的早熟问题,提高算法的全局搜索能力,并且采用非线性物种迁移模型以适应不同的自然环境。采用4个测试函数进行仿真,结果表明:HMBBO算法与标准BBO算法、基于Gauss变异及基于Cauchy变异的BBO算法比较,其收敛速度和全局寻优能力有明显改善。汽油调合和调度优化实例表明,该算法能够快速有效地找到全局最优解。     

改进的差分进化算法及在聚丙烯牌号切换优化中的应用

针对差分进化算法早熟问题,提出一种改进差分进化算法,采用动态缩放因子解决优化过程中的变量约束问题,在进化过程中自动地调整控制参数取值以保证变量约束条件;引入聚集度作为参数评估种群分布的密集程度,增加一种新的变异算子在进化过程中根据聚集度情况对部分个体进行后续变异操作,适时调整种群分布,提高种群多样性,增强全局搜索能力。建立了聚丙烯牌号切换优化模型并将改进的差分进化算法应用于牌号切换优化模型的求解,仿真实验结果表明改进的差分进化算法在全局搜索能力和搜索效率两个方面有较大提高。     

基于知识及遗传退火混合算法的翅片管换热器管路优化方法

通过在现有方法中引入专业知识及模拟退火算法;提出了一种基于知识及遗传退火混合算法的换热器管路连接优化方法。以一个实际翅片管换热器为例;在满足实际制造工艺的约束条件下;以换热器换热能力最大为优化目标对提出的方法进行了验证。结果表明;基于知识及遗传退火混合算法得到的最优管路连接中;各支路均匀交叉分布于空气流中;且所含换热管数目相等;避免了流体在不同支路之间换热不平衡的问题;优化后得到换热器的换热能力比优化前提高22％;比单纯基于遗传算法的优化方法得到换热器的换热能力提高10.3％。     

Multiobjective optimization of polymerization reaction of vinyl acetate by genetic algorithm technique with a new replacement criterion

A multiobjective optimization procedure based on genetic algorithm has been developed to determine optimum operational conditions of polymerization reaction. In this article by using a new selection criterion to choose the next generation members with better quality, optimization efficiency is improved and the number of generations to obtain Pareto optimal set reduced. In this proposed method a novel replacement criterion based on ranking level information and proximity of solutions to the Pareto optimal front is used to choose the next generation members. The polymerization of vinyl acetate has been chosen as an example. Two objective functions, which used in this study, are maximization of the weight average molecular weight up to the desired value and minimization of the residual initiator concentration. A Pareto optimal set of objective functions has been obtained by application of a Pareto set filter operator. Furthermore, the influence of genetic algorithm parameters on the efficiency and convergence of genetic algorithm is studied by changing cross over and mutation probabilities. Because of the flexibility and generality of genetic algorithm, this optimization method is a useful technique with lots of potentials in determination of optimum value of operation parameters. POLYM. ENG. SCI., 2008. © 2008 Society of Plastics Engineers     

Inferential Control of Reactive Destillation Columns – An Algorithmic Approach

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. Here, mixed integer optimization is used to determine the optimal control structure and the optimal controller parameters simultaneously. The process dynamics is included explicitly into the constraints using a rigorous nonlinear dynamic process model. Depending on the objective function, which is used for the evaluation of competing control systems, two different formulations are proposed which lead to mixed‐integer dynamic optimization (MIDO) problems. A MIDO solution strategy based on the sequential approach is adopted in the present paper. Here, the MIDO problem is decomposed into a series of nonlinear programming (NLP) subproblems (dynamic optimization) where the binary variables are fixed, and mixed‐integer linear programming (MILP) master problems which determine a new binary configuration for the next NLP subproblem. The proposed methodology is applied to inferential control of reactive distillation columns as a challenging benchmark problem for chemical process control.     

Optimization of membrane gas separation systems using genetic algorithm

Genetic algorithm is applied for the optimization of the membrane gas separation systems. Air separation for enriched oxygen production is the selected system for investigation. Optimizations for single and triple objective functions are studied. The optimization problem involves the selection of the optimal system configurations from three alternatives, including continuous membrane column (CMC), single stripper permeator (SSP), and two stripper in series permeator (TSSP), as well as the optimal operating conditions. Models of the three configurations and the genetic algorithm procedure are computerized. The objective functions discussed are the Rony separation index, power consumption per unit equivalent pure oxygen, and the membrane area. Both high-pressure and low-pressure (vacuum) operation modes are optimized and the effects of different oxygen product purity and feed rate are analyzed. For single objective function optimization, the solutions obtained using genetic algorithm are slightly inferior in one case but superior in other cases compared to those by pure mathematical optimization methods. For triple objective function optimization, the Pareto plots presenting multiple trade-off solutions are generated. In general, compared to high-pressure operation mode, the product recovery and power consumption for low-pressure operation mode are lower. For almost all the cases studied, CMC configuration with its high flexibility appears in the optimal solutions.     

Optimal grade transition and selection of closed-loop controllers in a gas-phase olefin polymerization fluidized bed reactor

To satisfy the diverse product quality specifications required by the broad range of polyolefin applications, polymerization plants are forced to operate under frequent grade transition policies. Commonly, the optimal solution to this problem is based on the minimization of a suitable objective function defined in terms of the changeover time, product quality specifications, process safety constraints and the amount of off-spec polymer, using dynamic optimization methods. However, considering the great impact that a given control structure configuration can have on the process operability and product quality optimization, the time optimal grade transition problem needs to be solved in parallel with the optimal selection of the closed-loop control pairings between the controlled and manipulated variables. In the present study, a mixed integer dynamic optimization approach is applied to a catalytic gas-phase ethylene-1-butene copolymerization fluidized bed reactor (FBR) to calculate both the “best” closed-loop control configuration and the time optimal grade transition policies. The gPROMS/gOPT computational tools for modelling and dynamic optimization, and the GAMS/CPLEX MILP solver are employed for the solution of the combined optimization problem. Simulation results are presented showing the significant quality and economic benefits that can be achieved through the application of the proposed integrated approach to the optimal grade transition problem for a gas-phase polyolefin FBR.     

An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems

Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA) based on a novel selection strategy is presented to handle nonlinear programming problems. Each individual in selection process is represented as a three-dimensional feature vector composed of objective function value, the degree of constraints violations and the number of constraints violations. We can distinguish excellent individuals through two indices according to Pareto partial order. Additionally, IGA incorporates a local search (LS) process into selection operation so as to find feasible solutions located in neighboring areas of some infeasible solutions. Experimental results over a set of benchmark problems demonstrate that proposed IGA has better robustness, effectiveness and stableness than other algorithm reported in literature.     

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。     

最优自适应广义预测控制在常压加热炉中的应用

对常压加热炉的最优操作条件和最优自适应广义预测控制进行研究。维持过剩空气系数在最优值附近，能够有效提高常压加热炉的热效率。针对燃烧控制与优化的特殊要求，通过在线优化方法得到最优值，并采用广义预测控制算法实施控制。引入阶梯式和自校正控制策略后，系统的鲁棒性和抗干扰能力得到改善。     

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.     

Sensor network design for maximizing process efficiency: An algorithm and its application

Sensor network design (SND) is a constrained optimization problem requiring systematic and effective solution algorithms for determining where best to locate sensors. A SND algorithm is developed for maximizing plant efficiency for an estimator‐based control system while simultaneously satisfying accuracy requirements for the desired process measurements. The SND problem formulation leads to a mixed integer nonlinear programming (MINLP) optimization that is difficult to solve for large‐scale system applications. Therefore, a sequential approach is developed to solve the MINLP problem, where the integer problem for sensor selection is solved using the genetic algorithm while the nonlinear programming problem including convergence of the “tear stream” in the estimator‐based control system is solved using the direct substitution method. The SND algorithm is then successfully applied to a large scale, highly integrated chemical process. © 2014 American Institute of Chemical Engineers AIChE J, 61: 464–476, 2015     

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.