DNA Double Helix Based Hybrid GA for the Gasoline Blending Recipe Optimization Problem

A hybrid genetic algorithm is proposed for heavily nonlinear constrained optimization problems by utilizing the global exploration and local exploitation characteristics, and the convergence rate of the proposed algorithm is analyzed. In the global exploration phase, a DNA double helix structure is used to overcome Hamming cliffs and DNA computing based operators are applied to improve the global searching capability. When the feasible domains are located, the sequential quadratic programming (SQP) method is performed to quickly find the local optimum and improve the solution accuracy. The comparison results of typical numerical examples and the gasoline blend recipe optimization problem are employed to demonstrate the reliability and efficiency of the proposed algorithm.     

基于差分进化和分布估计的改进混合算法在NLP及MINLP工程优化问题中的应用(英文)

In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.     

基于微粒群优化算法的不确定性调和调度

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization （PSO） algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.     

基于遗传算法和粒子群优化算法的电力系统无功优化

从数学的角度分析,电力系统无功优化是一个多变量、多约束、非连续性的混合非线性规划问题,因此,优化过程十分复杂.以减少有功网损为目标函数建立电力系统无功优化计算的数学模型,基于遗传算法和粒子群优化算法,提出一种新颖的混合策略来求解无功优化问题.IEEE 6和IEEE 14节点系统的仿真计算结果表明:与单一的遗传算法或粒子群优化算法相比,该混合策略在优化效果方面具有明显的优势.     

自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用

对于软测量模型参数估计问题, 针对传统梯度法求解非线性最小二乘模型时依赖初值、需要追加趋势分析进行验证和无法直接求解复杂问题的缺陷, 提出将参数估计化为约束优化问题, 使用混合优化算法求解的新思路。为此提出一种自适应混合粒子群约束优化算法(AHPSO-C)。在AHPSO-C算法中, 为平衡全局搜索(混沌粒子群)和局部搜索(内点法), 引入自适应内点法最大函数评价次数更新策略。对12个经典测试函数的仿真结果表明, AHPSO-C是求解约束优化问题的一种有效算法。将算法用于淤浆法高密度聚乙烯(HDPE)串级反应过程中熔融指数软测量模型参数估计, 验证了方法的可行性与优越性。     

基于收敛深度控制的多元混合非线性规划算法

随着对象模型描述的系统性和完整性的提高,过程优化问题的复杂程度逐步增加,对优化算法的性能提出了更高的要求。现有的非线性规划算法在求解性能上各有优劣,本文提出了一种基于收敛深度控制的多元混合非线性规划算法,将各个非线性规划算法视为元算法,利用收敛深度来控制这些元算法之间的相互协作,更好地发挥元算法各自的优势,从而提高求解大规模复杂优化问题的能力。采用空分系统的数据校正问题以及脱丙烷塔和脱丁烷塔联塔系统的优化问题对多元混合算法进行了测试,数值结果表明相比各个单独的非线性规划算法而言,多元混合算法具有更好的求解性能。     

两群微粒群算法及其在油品调和优化中的应用

针对复杂的非线性约束优化问题,提出了一种含变异算子的两群微粒群算法。算法构造了两个粒子群,分别设置了不同的搜索速度上限,并设计了粒子群间的协调机制和变异算子,使算法的寻优能力得到增强。针对油品调和配方优化进行了实例仿真,研究结果表明所提出的算法能获得较理想的调和配方,在满足调和利润最大的同时能保证对调和指标的卡边,使调和成品油的指标富余量大大降低。     

一种改进的混合遗传算法及在化工过程动态优化中的应用(英文)

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. Genetic algorithm (GA) has been proved to be a feasible method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm (HIGA) for solving dynamic optimization problems is proposed to overcome these defects. Simplex method (SM) is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Gaussian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.     

Control Vector Parameterization‐Based Adaptive Invasive Weed Optimization for Dynamic Processes

A novel optimal approach named invasive weed optimization‐control vector parameterization (IWO‐CVP) for chemical dynamic optimization problems is proposed where CVP is used to transform the problem into a nonlinear programming (NLP) problem and an IWO algorithm is then applied to tackle the NLP problem. To improve efficiency, a new adaptive dispersion IWO‐based approach (ADIWO‐CVP) is further suggested to maintain the exploration ability of the algorithm throughout the entire searching procedure. Several classic chemical dynamic optimization problems are tested and detailed comparisons are carried out among ADIWO‐CVP, IWO‐CVP, and other methods. The research results demonstrate that ADIWO‐CVP not only is efficient, but also outperforms IWO‐CVP in terms of both accuracy and convergence speed.     

改进鲸鱼优化算法及其在渣油加氢参数优化的应用

针对智能优化算法在处理非线性优化问题中存在的容易陷入局部最优和收敛精度差等问题,提出了一种基于结合差分进化和精英反向学习的改进鲸鱼算法（DEOBWOA）。该算法引入对立搜索初始化、精英反向学习,并结合差分进化进行变异修正,显著有效地提高WOA算法的收敛精度和收敛速度,提高其跳出局部最优的能力。之后采用8个标准测试函数进行仿真实验,结果表明：DEOBWOA算法与标准WOA、HCLPSO、DE算法相比,全局搜索能力和收敛速度都有较大提升。最后建立了渣油加氢动力学模型,考虑到渣油加氢过程中存在诸多典型的非线性约束问题,以某炼化厂渣油加氢装置为例,应用DEOBWOA对渣油加氢反应动力学模型参数进行优化,结果表明该算法能较好地处理实际工程优化问题。     

改进的膜计算仿生优化算法及在汽油调和中的应用

为提高膜计算仿生优化算法在求解流程工业复杂优化问题的计算性能,提出一种改进的膜计算仿生优化算法。该算法采用一个新的不确定性提取规则取代改进前的提取规则。4个有约束标准测试函数被用于检验该算法的计算性能,计算结果及对比显示了改进算法在鲁棒性和效率等方面优于改进前算法。改进算法应用于汽油调和优化问题,更高利润的配方及算法的计算效率证实了改进算法的优越性和实用性。     

一种基于并行计算的混合算法优化有分流换热网络

换热网络优化是化工过程系统工程领域的研究难点,其数学模型具有高度的非凸、非线性,在使用单一启发式算法优化时,往往具有局限性。研究以换热网络的年综合费用最小为目标,针对强制进化随机游走（RWCE）算法在优化时由于个体间独立进化,导致优化过程中信息缺乏交流的问题,提出将遗传算法（GA）与其混合。混合后的算法在保持前一半优势种群中的个体单独进化的基础上,通过周期性的交叉、变异等操作产生子代来替换掉劣势种群,从而增强了原有算法的整型变量优化能力,并弥补了弱势个体无法更新的不足。为了兼顾算法在大种群下优化有分流换热网络的计算效率,节约时间成本,使用OpenMP系统将混合算法实现了并行化设计。通过三个不同规模的换热网络问题对并行后的混合算法进行验证,结果表明该算法能在有效提升优化质量的前提下相比串行算法大幅缩短计算时间,其中两个算例突破了目前文献最优解。     

模块环境下的filter-SQP用于过程优化

引言 20世纪70年代中期以来,经过许多学者的努力,SQP法成为求解非线性规划(NLP)问题最有效的方法之一.     

汽油调合调度优化

采用连续时间建模方法，建立了一种新的汽油非线性调合和调度集成优化的混合整数非线性规划（MINLP）模型，克服了当前在油品调合调度中采用线性调合模型或者将非线性调合过程和调度分开优化的缺陷。针对建立MINLP模型的特点，将原MINLP问题转化为求解一系列的混合整数线性规划（MILP）模型，避免了直接求解MINLP模型的复杂性。最后以某大型炼油企业为例，验证了模型和算法的实用性。     

基于活跃约束条件辅助目标的进化算法求解油品调合问题

根据油品调合问题的特点,提出了一种基于活跃约束条件辅助目标的求解约束优化问题的新方法。该方法根据进化算法种群中的可行解和不可行解共同辨识约束优化问题的活跃约束条件。然后,通过增加活跃约束条件辅助目标的方法将单目标约束优化问题转换为多目标约束优化问题进行求解。通过该方法,相应的进化算法可以利用油品调合问题的活跃约束条件信息,从而达到提高进化算法求解油品调合问题的搜索效率和避免局部最优解的目的。最后,通过仿真研究证实了该方法的有效性。     

Optimal robust optimization approximation for chance constrained optimization problem

Chance constraints are useful for modeling solution reliability in optimization under uncertainty. In general, solving chance constrained optimization problems is challenging and the existing methods for solving a chance constrained optimization problem largely rely on solving an approximation problem. Among the various approximation methods, robust optimization can provide safe and tractable analytical approximation. In this paper, we address the question of what is the optimal (least conservative) robust optimization approximation for the chance constrained optimization problems. A novel algorithm is proposed to find the smallest possible uncertainty set size that leads to the optimal robust optimization approximation. The proposed method first identifies the maximum set size that leads to feasible robust optimization problems and then identifies the best set size that leads to the desired probability of constraint satisfaction. Effectiveness of the proposed algorithm is demonstrated through a portfolio optimization problem, a production planning and a process scheduling problem.     

A novel adaptive surrogate modeling‐based algorithm for simultaneous optimization of sequential batch process scheduling and dynamic operations

A novel adaptive surrogate modeling‐based algorithm is proposed to solve the integrated scheduling and dynamic optimization problem for sequential batch processes. The integrated optimization problem is formulated as a large scale mixed‐integer nonlinear programming (MINLP) problem. To overcome the computational challenge of solving the integrated MINLP problem, an efficient solution algorithm based on the bilevel structure of the integrated problem is proposed. Because processing times and costs of each batch are the only linking variables between the scheduling and dynamic optimization problems, surrogate models based on piece‐wise linear functions are built for the dynamic optimization problems of each batch. These surrogate models are then updated adaptively, either by adding a new sampling point based on the solution of the previous iteration, or by doubling the upper bound of total processing time for the current surrogate model. The performance of the proposed method is demonstrated through the optimization of a multiproduct sequential batch process with seven units and up to five tasks. The results show that the proposed algorithm leads to a 31% higher profit than the sequential method. The proposed method also outperforms the full space simultaneous method by reducing the computational time by more than four orders of magnitude and returning a 9.59% higher profit. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4191–4209, 2015     

New MINLP formulation for the multiperiod pooling problem

The modeling of blending tank operations in petroleum refineries for the most profitable production of liquid fuels in a context of time‐varying supply and demand is addressed. A new mixed‐integer nonlinear programming formulation is proposed that using individual flows and split fractions as key model variables leads to a different set of nonconvex bilinear terms compared with the original work of Kolodziej et al. These are better handled by decomposition algorithms that divide the problem into integer and nonlinear components as well as by commercial solvers. In fact, BARON and GloMIQO can solve to global optimality all problems resulting from the new formulation and test problems from the literature. A tailored global optimization algorithm working with a tight mixed‐integer linear relaxation from multiparametric disaggregation achieves a similar performance. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3728–3738, 2015     

基于一类混合PSO算法的函数优化与模型降阶研究

为了克服传统微粒群优化(PSO)算法容易早熟收敛和陷入局部极小的缺点,通过对PSO算法特点和行为的分析,提出一类有机结合模拟退火(SA)算法和PSO算法的混合算法.混合算法不仅利用PSO的机制进行群体全局搜索,而且利用模拟退火的思想恰当地选择微粒的最好历史位置,保障了群体多样性,并有效平衡了算法的探索和趋化能力,进而改善了算法的优化性能.基于典型复杂函数优化问题和模型降阶问题的仿真结果表明,所提混合算法具有很好的优化质量、搜索效率和鲁棒性.     

一种求解化工动态优化问题的迭代自适应粒子群方法

智能优化方法因其简单、易实现且具有良好的全局搜索能力,在动态优化中的应用越来越广泛,但传统的智能方法收敛速度相对较慢。提出了一种迭代自适应粒子群优化方法（IAPSO）来求解一般的化工动态优化问题。首先通过控制变量参数化将原动态优化问题转化为非线性规划问题,再利用所提出的迭代自适应粒子群优化方法进行求解。相比传统的粒子群优化方法,该种迭代自适应粒子群优化方法具有收敛速度更快的优点,主要原因是：该算法根据粒子种群分布特性自适应调整参数;该算法通过缩减搜索空间并迭代使用粒子群算法搜索最优解。将提出的迭代自适应粒子群方法应用到多个经典动态优化问题中,测试结果表明,该方法简单、有效,精度高,且收敛速度比传统粒子群算法有显著提升。