基于粒子群算法的变循环发动机部件法建模及优化

针对变循环发动机部件法建模及优化问题,首先使用部件法对变循环发动机进行建模,列出发动机各部件匹配工作时,受制约的7个平衡方程;然后,根据发动机工作时的已知条件以及发动机的部件法数学模型,推导出以7个平衡方程为基础的非线性方程组,并使用粒子群算法求解非线性方程组,实现变循环发动机部件法建模及优化;最后,对模型进行了评价并提出了改进方法.结果表明,粒子群算法对于求解变循环发动机非线性方程组具有较好的收敛性     

解信赖域子问题的隐式分段折线算法

在Hessian矩阵正定的前提下,建立了一种最优曲线的微分方程模型.针对此微分方程模型,构造了一条隐式分段折线,从而提出了一种求解信赖域子问题的隐式分段折线算法,并且分析和证明了隐式分段折线路径的合理性.数值结果表明新算法是有效且可行的.     

基于遗传算法的变循环航空发动机部件级建模与优化

研究了变循环发动机双涵道(涡扇)模式和单涵道(涡喷)模式的工作机理,分别建立了相应模式下的部件模型和整机气动热力学模型,利用遗传算法解决高复杂性、多变量非线性方程问题的优点,对各个模型进行求解与优化,并获得了相应结果,验证了模型的有效性.     

带平衡约束的离散网络平衡设计问题的遗传算法

本文研究了带平衡约束的离散网络设计问题及其求解算法.模型中上层是一个离散网络设计的数学规划模型,采用遗传算法来求解.下层是采用变分不等式描述的用户平衡配流问题,利用对角化方法直接求解.通过实例对算法进行验证,结果表明该算法是有效的.     

变循环发动机部件法建模及优化

对2013年全国研究生数学建模竞赛A题"变循环发动机部件法建模及优化"的问题进行建模及求解.通过模型设计出逐维线性插值法对风扇和CDFS的几何特性进行研究.利用阻尼牛顿迭代法对共同工作方程组进行求解.运用非线性规划约束优化算法对发动机的性能进行优化.然后通过数值仿真验证了提出的算法的有效性.     

双势理论用于处理非关联材料本构

基于传统塑性力学框架下的显式积分算法和基于Simo-Taylor提出的回退映射隐式积分算法是固体力学中两大经典本构积分算法.以经典的非关联材料模型Drucker-Prager(D-P)模型和Armstrong-Frederick(A-F)模型为例分别回顾了显式积分算法和隐式积分算法.以双势理论为基础,将双势的概念运用到材料的自由能中,将材料分为显式标准材料和隐式标准材料.两种传统积分算法都能有效地处理显式标准材料的本构关系,但在处理隐式标准材料时却存在一定的问题.双势积分算法是建立在双势理论下的本构积分算法,此算法不仅能够处理显式标准材料,对于处理隐式标准材料,也存在一定的优势.通过变分原理推导了双势积分算法解的存在性,运用双势积分算法处理Drucker-Prager模型和Armstrong-Frederick模型,并与经典传统积分算法得到的结果进行对比,验证了双势本构积分算法的稳定性和准确性.     

一般混合似变分不等式的隐式迭代算法

对一般混合似变分不等式的若干隐式迭代算法进行了研究;利用一般混合似变分不等式与不动点问题和预解方程的等价关系,采用分裂技巧和自适应迭代技巧结合,提出了一个求解一般混合似变分不等式的新的隐式迭代算法;并证明了该算法在算子T是g-单调连续的条件下收敛．     

电力系统中地网腐蚀诊断的一种新的数学模型及其仿真计算

对电力系统中具有重大应用价值的地网腐蚀诊断问题抽象出仿真求解的一种新的数学模型:即求解带约束的非线性隐式方程组模型.但由于问题本身的物理特性决定了所建立的数学模型具有以下特点:一是非线性方程组为欠定方程组,而且非线性程度非常高;二是方程组的所有函数均为隐函数;三是方程组附加若干箱约束条件.这种特性给模型分析与算法设计带来巨大困难.对于欠定方程组的求解,文中根据工程实际背景,尽可能地扩充方程的个数,使之成为超定方程组,然后对欠定方程组和超定方程组分别求解并进行比较.将带约束的非线性隐函数方程组求解问题,转化为无约束非线性最小二乘问题,并采用矩阵求导等技术和各种算法设计技巧克服隐函数的计算困难,最后使用拟牛顿信赖域方法进行计算.大量的计算实例表明,文中所提出的数学模型及求解方法是可行的.与目前广泛采用的工程简化模型相比较,在模型和算法上具有很大优势.     

一种新的有杆抽油泵功图诊断模型及计算方法

通过对多级杆柱上的微元体进行动力学分析,对现有的Gibbs模型进行了改进,建立了一个新的有杆抽油泵功图诊断数学模型.采用隐式差分格式和变步长有限差分格式对模型进行求解,得到了接箍上、下分界面处和抽油杆均质段任意位置处的位移及载荷表达式,推导了等效阻尼系数的计算公式,建立了相应的迭代算法来计算井下泵功图.以某油井地面示功图为例,利用该模型进行了实例计算,与Gibbs方法对比结果表明,该模型及算法得到的泵功图更接近实际.     

扩散问题的一类带有加权系数的隐格式及多子域并行算法

针对扩散问题提出了一类带有加权系数的隐格式,采用分组显式和区域分解思想,又构造了若干分组显式格式.结合初边值条件,建立了求解扩散问题的一种多子域并行算法.虽然格式是隐式的,但在算法实现过程中可显式且并行地计算,这样避免了求解线性方程组的复杂性.并且当加权系数1≤θ≤2.4时,格式是无条件稳定的;0θ1时,趋向于1的方向,格式也是无条件稳定的;θ=2时,算法收敛的最快,收敛速率接近于2.通过数值试验证明此类隐格式和并行算法是有效的,计算速度快,精确度高,易于实现并行.     

DEVS approximation of infiltration using genetic algorithm optimization of a fuzzy system

The infiltration process is generally described by a nonlinear differential equation, which can be solved by iteration methods such as a Newton-Raphson method. In this paper we propose a Discrete Event System Specification (DEVS) model for Green-Ampt infiltration. We show that this model can be approximated using Genetic Algorithm optimization of a fuzzy system. The fuzzy approximation is shown to be more accurate than the Taylor series approximation recently proposed.     

Solving multi-objective parallel machine scheduling problem by a modified NSGA-II

In this paper, we modify a Multi-Objective Evolutionary Algorithm, known as Nondominated sorting Genetic Algorithm-II (NSGA-II) for a parallel machine scheduling problem with three objectives. The objectives are – (1) minimization of total cost due tardiness, (2) minimization of the deterioration cost and (3) minimization of makespan. The formulated problem has been solved by three Multi-Objective Evolutionary Algorithms which are: (1) the original NSGA-II (Non-dominated Sorting Genetic Algorithm–II), (2) SPEA2 (Strength Pareto Evolutionary Algorithm-2) and (3) a modified version of NSGA-II as proposed in this paper. A new mutation algorithm has also been proposed depending on the type of problem and embedded in the modified NSGA-II. The results of the three algorithms have been compared and conclusions have been drawn. The modified NSGA-II is observed to perform better than the original NSGA-II. Besides, the proposed mutation algorithm also works effectively, as evident from the experimental results.     

Hybrid variable neighbourhood approaches to university exam timetabling

In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects appropriate neighbourhoods to use within the biased VNS, produces the best known results in the literature, in terms of solution quality, on some of the benchmark instances. However, it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed.     

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.     

急件订单干扰下虚拟单元重调度

虚拟单元生产中,针对急件订单干扰情况,研究了考虑序位相似性,即尽量保持初始工序的加工次序的虚拟单元重调度问题。为了应对急件订单干扰,设置了各工件工序可用机器集合和相应的加工时间集合,构建了以序位相似性最大和急件订单完工时间、系统总流程时间最短为目标的多目标非线性整数规划模型。针对模型自身特征,采用了遗传—蚁群算法相结合的优化算法求解模型。最后,以船舶实际生产为例,验证了模型的可行和优越性,以及算法的有效性。     

Multi-item multi-period optimal production problem with variable preparation time in fuzzy stochastic environment

In this paper, a multi-item multi-period optimal production control problem with variable preparation time and limited available space is formulated and solved. Here, the rate of production is assumed to be a function of time and considered as a control variable. Also the demand is linearly stock dependent. The preparation time is assumed and considered to be a variable. Production and set-up costs are dependent on preparation time. Here, preparation time influences the production cost negatively and set-up cost positively. Also the space constraint is assumed to be fuzzy-random in nature and with the help of Mean Chance Constraint Method, the fuzzy-random space constraint is converted to a crisp one. This problem is formulated as an optimal control problem and solved with the help of Genetic Algorithm (GA). Best optimum and the second best optimum results are obtained and these are also presented in tabular forms and graphically.     

变循环发动机部件法建模及优化述评

叙述了2013年全国研究生数学建模竞赛A题"变循环发动机部件法建模及优化"的命题背景和目的,分析了本赛题的建模及求解思路,总结了参赛队的一些好的做法和评阅过程中发现的一些问题,最后叙述了本赛题还需要继续思考的问题.     

SASEGASA: A New Generic Parallel Evolutionary Algorithm for Achieving Highest Quality Results

This paper presents a new generic Evolutionary Algorithm (EA) for retarding the unwanted effects of premature convergence. This is accomplished by a combination of interacting generic methods. These generalizations of a Genetic Algorithm (GA) are inspired by population genetics and take advantage of the interactions between genetic drift and migration. In this regard a new selection scheme is introduced, which is designed to directedly control genetic drift within the population by advantageous self-adaptive selection pressure steering. Additionally this new selection model enables a quite intuitive heuristics to detect premature convergence. Based upon this newly postulated basic principle the new selection mechanism is combined with the already proposed Segregative Genetic Algorithm (SEGA), an advanced Genetic Algorithm (GA) that introduces parallelism mainly to improve global solution quality. As a whole, a new generic evolutionary algorithm (SASEGASA) is introduced. The performance of the algorithm is evaluated on a set of characteristic benchmark problems. Computational results show that the new method is capable of producing highest quality solutions without any problem-specific additions.     

Two storage inventory problem with dynamic demand and interval valued lead-time over finite time horizon under inflation and time-value of money

A finite time horizon inventory problem for a deteriorating item having two separate warehouses, one is a own warehouse (OW) of finite dimension and other a rented warehouse (RW), is developed with interval-valued lead-time under inflation and time value of money. Due to different preserving facilities and storage environment, inventory holding cost is considered to be different in different warehouses. The demand rate of item is increasing with time at a decreasing rate. Shortages are allowed in each cycle and backlogged them partially. Shortages may or may not be allowed in the last cycle and under this circumstance, there may be three different types of model. Here it is assumed that the replenishment cycle lengths are of equal length and the stocks of RW are transported to OW in continuous release pattern. For each model, different scenarios are depicted depending upon the re-order point for the next lot. Representing the lead-time by an interval number and using the interval arithmetic, the single objective function for profit is changed to corresponding multi-objective functions. These functions are maximized and solved by Fast and Elitist Multi-objective Genetic Algorithm (FEMGA). The models are illustrated numerically and the results are presented in tabular form.     

A stochastic approach for the optimization of open-loop engine control systems

The operation of sensors and actuators in engine control systems is always affected by errors, which are stochastic in nature. In this paper it is shown that, because of the non-linear interactions between engine performance and control laws in an open-loop engine control system, these errors can give rise to unexpected deviations of control variables, fuel consumption and emissions from the optimal values, which are not predictable in an elementary way.A model for vehicle performance evaluation on a driving cycle is presented, which provides the expected values of fuel consumption and emissions in the case of stochastic errors in sensors and actuators, utilizing only steady-state engine data.The stochastic model is utilized to obtain the optimal control laws; the resultant non-linear constrained minimization problem is solved by an Augmented Lagrangian approach, using a Quasi-Newton technique. The results of the stochastic optimization analysis indicate that significant reductions in performance degradation may be achieved with respect to the solutions provided by the classical deterministic approach.