基于蚂蚁智能体调度的混沌搜索算法及化工应用

针对混沌搜索随机性的缺点和遍历性的优点,提出了一种基于蚂蚁智能体调度的混沌搜索算法（chaos optimization algorithm based on ant agent scheduling,CAAS）。该算法将解空间的每维变量都划分成若干子域并分配一定规模的蚂蚁智能体,蚂蚁智能体在各子域中进行混沌搜索。同时,根据每维变量各个子域中信息素浓度决定蚂蚁智能体在各个子域间的转移,以有效克服传统混沌优化算法的随机性,实现快速的全局最优搜索。分别采用传统混沌优化算法和CAAS对标准的非线性连续优化问题进行寻优。结果表明：CAAS的全局搜索性能、收敛速率都明显地优于混沌优化算法。最后,将该算法应用于对羧基苯甲醛含量软测量模型参数估计,取得良好的效果。     

基于Alopex的进化算法及其在乙炔加氢反应器建模中的应用

分析进化优化算法的共性,参考Alopex优化思想提出一种新的进化优化算法.给出该算法的具体步骤,将该算法与标准粒子群算法、遗传算法和差分算法在性能上做了仿真时比,并将其应用于乙炔加氢反应器出口乙炔浓度软测量的建模中.这种新的优化算法具有全局收敛能力,并具有较快的收敛速度.对典型函数的测试和基于神经网络的软测量建模表明:新算法具有较强的全局搜索能力,特别是对易于陷入局部最优的多峰函数能够有效地避免早熟收敛问题.     

基于混合粒子群算法的复杂相平衡计算方法

对于含有两个部分互溶液相的相平衡问题,采用经典方法收敛困难或易陷于平凡解。为此根据最小Gibbs自由能原理,提出采用混合粒子群算法搜索全局最优解,计算得到系统的最小Gibbs自由能状态,实现复杂相平衡计算。通过改建目标函数,减少计算量,并引入组分相分率,将物料平衡约束转换为规范型立方空间的优化问题,适于粒子群算法搜索。在常规粒子群算法中引入Nelder-Mead单纯形操作,可显著提高搜优的速率和精度。将其应用于甲苯-水-苯胺液液平衡和苯-乙腈-水汽液液平衡计算,取得了良好的效果。     

极大极小值交替优化算法实现换热网络全局热集成

针对确定性方法应用于换热网络全局热集成时易陷入局部极值的问题,采用乘子法建立辅助函数,将原来的换热网络有约束问题转化为无约束问题,在此基础上提出了基于牛顿法的极大、极小值交替优化算法应用于换热网络优化。该算法通过优化进程中极大值、极小值的交替计算,不断跳出当前的局部极小值并继续通过局部优化方法求解新一轮的局部极小值,从而实现换热网络的全局热集成。同时提出防止"回跳"策略,避免该算法计算过程中在某个区域重复优化的问题。将算法应用于两个经典换热网络实例,取得了较好的结果,验证了极大、极小值交替优化算法能够有效地改善确定性方法易陷入局部极值的问题,具有较强的全局搜索能力,使优化质量较文献得到了进一步提升。     

优进策略支持的进化规划估计反应动力学参数

为准确估计反应动力学参数,在分析确定性优化方法与进化算法特性的基础上,提出了一种由优进策略支持的进化规划方法(EEP),它将确定性寻优的两点梯度法(TPG)引入随机的进化规划算法(EP)中。EEP将依概率调用TPG寻优操作,并相应地调整原有的随机性操作,包括简化变异操作、改进选择操作。测试结果表明EEP克服了TPG与EP的缺点,发扬了二者的优点,具有良好的全局寻优性能。将EEP方法成功地应用于2-氯苯酚在超临界水中氧化反应动力学参数的估计,效果良好,与其它方法相比,结果有所改进,显示出EEP方法的优越性。     

全局群搜索优化算法及其在汽油调合中的应用

汽油调合配比生产优化是一种非线性约束的多峰优化问题。针对一般群智能优化算法在解决此类优化中易陷于局部最优解,提出了一种改进的群搜索优化算法--全局群搜索优化算法（GGSO）。该算法采用混沌机制初始化粒子在解空间内均匀分布;在算法前期,保留GSO的追随者进化策略,以保证算法的收敛速度。在算法后期,对追随者引入速度更新和个体最优,以保证算法的收敛精度;在粒子陷入局部极值时,对追随者和游荡者引入一种新的交叉、变异机制和自适应混沌扰动机制,以保证粒子跳出局部极值,提高算法全局寻优性能。分别用4个标准测试函数对优化算法进行测试,结果表明：GGSO算法与标准GSO、线性递减惯性权重粒子群算法（LDWPSO）比较,收敛速度和全局寻优性能有明显优势。汽油在线调合优化实例应用表明：该算法有较快的收敛速度,能够较准确地寻得全局最优。     

混合免疫算法及在相平衡计算中的应用

带或不带化学反应的相平衡计算为化学、化工领域的重要课题;可将其转换为带有约束的Gibbs自由能最小化问题。常用的序贯二次规划（SQP）收敛速度快;但依赖初始值;易陷入局部极小。人工免疫算法（AIS）具有全局寻优功能;但局部搜优性能差;收敛速度很慢;甚难找到痕量解。为此;在AIS算法中引入SQP操作;汲取两者的优点;构建混合免疫算法（HAIS）。还将相平衡的物质的量变量转换为摩尔分数;并采用适当策略处理约束;以基本可行解为基础;快速生成满足约束的抗体;以提高HAIS的操作速率。多个相平衡实例应用表明HAIS性能良好;优于其他方法（SQP;AIS）。     

超临界甲醇＋甘油＋脂肪酸甲酯体系气液相平衡算法的改进

提出了一个使用PR状态方程计算超临界甲醇＋甘油＋脂肪酸甲酯体系气液平衡的快速算法。基于威尔逊关联式,利用单纯性法优化了相平衡常数的初始值;提出了一个判断是否处于气液两相区域的有效判断方法。与常规多元相平衡算法相比,新方法显著地减少了迭代次数,并拓展了算法的应用范围,甚至可以计算接近于单相区的气液平衡问题。     

基于LCPSO算法的相稳定性判别

相稳定性判别为相平衡计算的基本课题,常采用Gibbs自由能曲面与切平面的距离函数（TPDF）最小化方法求解。对于强非理想体系,或在高压条件下,其TPDF表现出复杂形态,有平凡解和多极值,传统方法难以求得满足约束的全局最小值,从而导致判别失误。粒子群算法（PSO）虽有全局优化性能,但也会陷于局部极小,且缺少约束处理机制。为此,分析了PSO内在蕴含的线性特点,在种群初化、粒子运动等环节提出应对策略,构建线性约束粒子群算法（LCPSO）,确保种群在可行空间内搜索。还增设调变参数、局部加速等措施,以兼顾算法的全面探测和细化挖掘的能力,提高其全局优化效能。经多个实例的测试表明,LCPSO适用面广,既可用于超额自由能、状态方程等各类热力学模型,又能克服混合模型一阶不连续的困难,应用范围从液液相分裂拓展到汽液液相分裂。与确定性全局算法IN/GB相比,LCPSO速率高,效果好,尤对多元体系更具优势。     

移动机器人全局路径规划算法综述

全局路径规划技术是移动机器人自主导航的关键技术之一,是机器人在路径规划领域的一项重点研究课题.通过对算法进行优化改进以提高移动机器人对环境信息获取的准确度、路径平滑性、减少冗余点和迭代次数便成为移动机器人全局路径规划算法的重点研究对象.本文将全局路径规划分为完备性规划算法和概率完备算法进行分析研究,同时对每种算法进行的...     

Global optimization methods for chemical process design: Deterministic and stochastic approaches

Process optimization often leads to nonconvex nonlinear programming problems, which may have multiple local optima. There are two major approaches to the identification of the global optimum: deterministic approach and stochastic approach. Algorithms based on the deterministic approach guarantee the global optimality of the obtained solution, but are usually applicable to small problems only. Algorithms based on the stochastic approach, which do not guarantee the global optimality, are applicable to large problems, but inefficient when nonlinear equality constraints are involved. This paper reviews representative deterministic and stochastic global optimization algorithms in order to evaluate their applicability to process design problems, which are generally large, and have many nonlinear equality constraints. Finally, modified stochastic methods are investigated, which use a deterministic local algorithm and a stochastic global algorithm together to be suitable for such problems. Partly presented at PSE Asia 2000 (December 6–8, Kyoto, Japan)     

Evaluation of Covariance Matrix Adaptation Evolution Strategy,Shuffled Complex Evolution and Firefly Algorithms for phase stability,phase equilibrium and chemical equilibrium problems

Phase equilibrium calculations and phase stability analysis of reactive and non-reactive systems play a significant role in the simulation, design and optimization of reaction and separation processes in chemical engineering. These challenging problems, which are often multivariable and non-convex, require global optimization methods for solving them. Stochastic global optimization algorithms have shown promise in providing reliable and efficient solutions for these thermodynamic problems. In this study, we evaluate three alternative global optimization algorithms for phase and chemical equilibrium calculations, namely, Covariant Matrix Adaptation-Evolution Strategy (CMA-ES), Shuffled Complex Evolution (SCE) and Firefly Algorithm (FA). The performance of these three stochastic algorithms was tested and compared to identify their relative strengths for phase equilibrium and phase stability problems. The phase equilibrium problems include both multi-component systems with and without chemical reactions. FA was found to be the most reliable among the three techniques, whereas CMA-ES can find the global minimum reliably and accurately even with a smaller number of iterations.     

Multiphase equilibria calculation by direct minimization of Gibbs free energy with a global optimization method

This paper presents a new method for multiphase equilibria calculation by direct minimization of the Gibbs free energy of multicomponent systems. The methods for multiphase equilibria calculation based on the equality of chemical potentials cannot guarantee the convergence to the correct solution since the problem is non-convex (with several local minima), and they can find only one for a given initial guess. The global optimization methods currently available are generally very expensive. A global optimization method called Tunneling, able to escape from local minima and saddle points is used here, and has shown to be able to find efficiently the global solution for all the hypothetical and real problems tested. The Tunneling method has two phases. In phase one, a local bounded optimization method is used to minimize the objective function. In phase two (tunnelization), either global optimality is ascertained, or a feasible initial estimate for a new minimization is generated. For the minimization step, a limited-memory quasi-Newton method is used. The calculation of multiphase equilibria is organized in a stepwise manner, combining phase stability analysis by minimization of the tangent plane distance function with phase splitting calculations. The problems addressed here are the vapor–liquid and liquid–liquid two-phase equilibria, three-phase vapor–liquid–liquid equilibria, and three-phase vapor–liquid–solid equilibria, for a variety of representative systems. The examples show the robustness of the proposed method even in the most difficult situations. The Tunneling method is found to be more efficient than other global optimization methods. The results showed the efficiency and reliability of the novel method for solving the multiphase equilibria and the global stability problems. Although we have used here a cubic equation of state model for Gibbs free energy, any other approach can be used, as the method is model independent.     

Surrogate-model-based,particle swarm optimization,and genetic algorithm techniques applied to the multiobjective operational problem of the fluid catalytic cracking process

Abstract

This article provides a concise multiobjective optimization methodology for an industrial fluid catalytic cracking unit (FCCU) considering stochastic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), based on surrogates or meta-models in order to approximate the objective function. A FCCU was considered and simulated in an AspenONE process simulator. In addition the article examines the claim that PSO has the same effectiveness (finding the optimal global solution) as GA, but with significantly better computational efficiency (fewer function evaluations). The optimization results obtained with the PSO technique, based on the evaluation of less functions and adjustment of less parameters, showed a 3% increase in yield of naphtha as compared to results obtained with the GA technique. Finally, the results of the optimization obtained with the stochastic optimization techniques were compared and analyzed with a deterministic one. The performance targets of the multiobjective operational optimization supported the FCCU design and production planning to ensure refinery profitability and a regulatory environment.     

随机搜索算法在换热网络优化中的应用进展

随着对能源利用率要求的不断提高,换热网络的优化已引起了人们的高度重视。本文综述了国内外应用随机搜索算法处理换热网络优化问题的最新研究进展,分析了当前应用较多的4种随机搜索算法：遗传算法、模拟退火算法、粒子群算法、禁忌搜索算法。阐述了这些算法各自的优势和尚待改进的问题,指出将不同算法结合起来解决大规模的换热网络优化问题是今后的研究方向。     

One-step approach for heat exchanger network retrofitting using integrated differential evolution

Heat exchanger network (HEN) retrofitting is more important and challenging than HEN synthesis since it involves modifying existing network for improved energy efficiency. Additional factors to be considered include spatial constraints, relocation and re-piping costs, reassignment and effective use of existing heat exchanger areas. The previous studies using stochastic global optimization algorithms are mainly focused on two-level approach: the first level uses a stochastic algorithm for optimizing structure, and the second level uses either a stochastic or a deterministic algorithm for optimizing continuous variables. In this study, we propose and test one-step approach where a stochastic global optimization method, namely, integrated differential evolution (IDE), handles both discrete and continuous variables together. Thus, HEN structure and retrofitting model parameters are simultaneously optimized by IDE, which avoids the algorithm trapping at a local optimum and also improves the computational efficiency. Results on HEN applications show that the proposed approach gives better solutions.     

应用广义简约梯度算法增强遗传算法求解化工冶金相平衡

研究可靠的相平衡计算对化工冶金过程有很重要意义,本文采用广义简约梯度法（GRG）来增强遗传算法（GA）用于相平衡计算过程,在遗传算法演化快结束时引入GRG算子,演化结束后使用GRG精修结果,克服了GA局部搜索能力不强的缺点,加快了搜索速度.KCl-FeCl₂体系相平衡计算结果显示这种混合算法（Hybrid Algorithms）能够提高GA效率的同时保留了全局收敛的优点,因此其在化工冶金相平衡计算中将有广泛的应用前景.     

Gravitational Search,Monkey, and Krill Herd Swarm Algorithms for Phase Stability,Phase Equilibrium,and Chemical Equilibrium Problems

Phase equilibrium calculations (PECs) and phase stability (PS) analysis of reactive and nonreactive systems problems are important for the simulation and design of chemical engineering processes. These problems, which are challenging, multi-variable, and non-convex, require optimization techniques that are both efficient and effective in finding the solution. Stochastic global optimization algorithms, especially swarm algorithms, are promising tools for such problems. In this study, monkey algorithm (MA), gravitational search algorithm (GSA), and Krill Herd algorithm (KHA) were used to solve PS, phase equilibrium, and chemical equilibrium problems. We have also studied the effect of adding a local optimizer at the end of the stochastic optimizer run. The results were compared to determine the strengths and weaknesses of each algorithm. When a local optimizer was used, MA was found to be a reliable algorithm in solving the problems. GSA had relatively the least numerical effort for all problems among the three algorithms but with low reliability. KHA was more reliable than other two algorithms without the use of a local optimizer. The performance of GSA, MA, and KHA was compared with firefly algorithm and cuckoo search (CS). In summary, this study found that CS algorithm was more reliable than the newly tested algorithms. Nevertheless, MA and GSA algorithms, when combined with a local optimizer, solve the thermodynamic problems as reliably and efficiently as CS.     

Capacity expansion planning through augmented Lagrangian optimization and scenario decomposition

Stochastic programming is a typical method for addressing the uncertainties in capacity expansion planning problem. However, the corresponding deterministic equivalent model is often intractable with considerable number of uncertainty scenarios especially for stochastic integer programming (SIP) based formulations. In this article, a hybrid solution framework consisting of augmented Lagrangian optimization and scenario decomposition algorithm is proposed to solve the SIP problem. The method divides the solution procedure into two phases, where traditional linearization based decomposition strategy and global optimization technique are applied to solve the relaxation problem successively. Using the proposed solution framework, a feasible solution of the original problem can be obtained after the first solution phase whereas the optimal solution is obtained after the second solution phase. The effectiveness of the proposed strategy is verified through a numerical example of two stage stochastic integer program and the capacity expansion planning examples. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Risk management for a global supply chain planning under uncertainty: Models and algorithms

In this article, we consider the risk management for mid‐term planning of a global multi‐product chemical supply chain under demand and freight rate uncertainty. A two‐stage stochastic linear programming approach is proposed within a multi‐period planning model that takes into account the production and inventory levels, transportation modes, times of shipments, and customer service levels. To investigate the potential improvement by using stochastic programming, we describe a simulation framework that relies on a rolling horizon approach. The studies suggest that at least 5% savings in the total real cost can be achieved compared with the deterministic case. In addition, an algorithm based on the multi‐cut L‐shaped method is proposed to effectively solve the resulting large scale industrial size problems. We also introduce risk management models by incorporating risk measures into the stochastic programming model, and multi‐objective optimization schemes are implemented to establish the tradeoffs between cost and risk. To demonstrate the effectiveness of the proposed stochastic models and decomposition algorithms, a case study of a realistic global chemical supply chain problem is presented. © 2009 American Institute of Chemical Engineers AIChE J, 2009