基于DEPSO 的工程项目增量净收益优化研究

针对当前工程项目网络计划制定中难以对工期、成本、质量三大目标进行有效统筹管控的问题。将这三大目标转化为增量净收益目标,解决由于各单目标之间量纲的不同而难以统筹优化问题,建立了增量净收益最大的工期—费用—质量模型,理清了它们之间错综复杂的关系和相互对立的局面。充分利用差异演化算法和粒子群算法的各自优点,提出了差异演化粒子群算法对上述多目标优化模型进行求解;并通过工程实例检验差异演化粒子群算法的有效性,计算结果显示,改进算法在很大程度上改善了求解性能,极大地提高了工程项目的净收益。     

基于交互式演化的海报设计方法

典型的现代优化方法只能解决显示优化问题,本文主要从隐式优化问题入手,利用交互式演化算法让用户参与目标个体评价的特点,计算出满足用户需求的最优个体,最后将此方法用在海报设计上,得出较好的结果。     

多目标规划模型的研究

优化问题是数学建模中的一类大问题,在历年全国大学生数学建模竞赛赛题中几乎离不开优化方法。多目标规划是大规模优化问题中最常见的一种,在现实的决策过程中,决策者面临的往往是一些具有多个目标的决策问题。多目标规划恰恰就是解决这类问题的理想方法,因而有着广泛的应用前景。本文从多目标规划的意义,发展现状,研究内容,技术难点,关键技术,研究方法,市场前景及社会效益等方面进行论述,使多目标规划模型在应用上更具有普遍性。     

基于区间数的不确定环境下的综合生产计划目标规划方法

提出了一种目标规划方法来解决各生产形式成本、客户需求和机器能力均含不确定参数的多目标多产品综合生产计划的决策问题。通过对区间数的优化处理和合理的目标规划,将不确定环境下的具有不同优先级的多目标优化问题转化为一个确定环境下的多优先级多目标的目标规划问题从而求解。     

基于微粒群算法的工程项目质量、费用和工期综合优化

进度、费用和质量称为工程项目的三大控制目标,三者之间相互依存、相互影响。工程项目控制的理想状态是同时实现合理的工期、较低的费用和较高的质量。微粒群算法(PSO)是新近出现的一种仿生算法,具有简单容易实现,而且随机搜索的优点,使得搜索不易陷于局部最优。将该算法引入工程项目优化领域,研究工程项目的质量、费用和工期的综合优化问题。系统介绍微粒群算法原理、流程以及算法的改进发展,研究工程项目质量、费用和工期的优化,并建立质量、费用和工期的多目标综合优化模型,介绍应用微粒群算法编码解决工程项目多目标优化的方法步骤。最后,通过一个应用实例,计算表明微粒群算法可以准确快速地解决工程项目多目标优化问题。     

基于离散PSO算法的医疗云存储部署策略

医疗云服务在实施的过程中,人们更加关注数据的安全、以及系统的负载均衡等问题。而解决这些问题的关键技术之一就是医疗云存储部署规划的优化策略。文章通过系统建模,将医疗云存储所涉及的相关技术抽象为多目标优化问题后,通过离散PSO算法进行求解。并引入新的粒子位置更新算法,以提高标准离散PSO算法的收敛速度,通过重优化机制来更新粒子状态,防止算法陷入局部最优而早熟,最终得到适合于求解医疗云存储部署优化问题的IDPSO算法。仿真实验表明该算法有效。     

基于演化-单纯形算法和结构物理响应反演结构物理参数的方法

演化计算是上世纪 60年代后基于生物进化的思想发展起来的一类求解优化问题的自适应、自组织、随机搜索的全局优化算法。针对遗传算法、演化策略两种演化计算存在的问题 ,文献 [1]将演化计算与单纯形算法相结合 ,设计了一种基于浮点编码的演化 -单纯形算法。本文将演化 -单纯形算法应用于结构动态反应下的结构物理参数反演分析 ,建立了一种基于时间域实测的结构部分物理响应的结构物理参数反演分析方法。为提高反分析效率 ,文中将物理坐标空间问题转换到模态空间来求解 ,提出了模态演化 -单纯形算法。通过与其它方法的算例比较表明 :本文提出的反演分析方法有精度和搜索效率高、抗燥能力强、对不完全信息情况有很好的适应性等优点。     

微分演化算法在桁架形状优化中的应用

为了获得全局最优和解决具有应力约束、几何约束以及局部稳定性约束的桁架形状优化问题中2类不同设计变量耦合给优化带来的困难,将1种新型智能优化算法——微分演化(Differential Evolution,DE)应用于桁架结构的形状优化问题中。给出了考虑节点坐标和截面面积两类不同性质的设计变量的桁架结构优化的数学模型,并对几个经典的桁架结构进行优化,将所得结果与其他优化算法结果进行了比较。数值结果表明了DE算法具有良好的收敛性和稳定性,可以有效地进行桁架结构的形状优化设计。     

基于NSGA-Ⅱ的网架结构多目标抗震优化

针对网架结构抗震设计,提出一种基于多目标进化算法NSGA-Ⅱ的抗震优化设计方法。建立以网架用钢量最小化和结构最大竖直位移最小化为目标的多目标优化模型;结合有限元动力计算模型和显式动力分析方法对网架结构进行地震响应分析;考虑到优化模型带有约束条件以及设计变量的特点,提出基于NSGA-Ⅱ的改进算法NSGA-Ⅱ+,采用该算法来求解优化模型。为了验证算法改进效果,对经典的空间桁架结构进行了优化,并与其他算法的优化结果进行比较,结果表明改进算法具有良好的收敛性和稳定性。最后以ANSYS/LS-DYNA为结构动力分析工具,混合编程实现了一个斜放四角锥网架的多目标抗震优化设计。优化实例表明,该方法能得到优质解,具有实际工程意义。     

快速群搜索算法用于桁架结构多目标优化北大核心CSCD

基于快速群搜索优化算法QGSO(Quick Group Search Optimizer),结合Pareto最优解理论与拥挤距离机制,提出了一种适用于结构优化的简单而实用的多目标算法:多目标快速群搜索优化算法MQGSO(Multi-objective Quick Group Search Optimization),并将其应用于10杆平面桁架结构的截面优化与25杆空间桁架结构的形状优化.将MQGSO算法与已有的MGSO和IMGSO算法进行了比较,发现其非劣解均优于其他两个算法的非劣解,且具有良好的稳定性与收敛精度,收敛速度也有所提高,说明本文提出的算法用于求解结构多目标优化问题是可行有效的.     

空间桁架多目标优化方法的比较研究

利用数值结果比较研究了包括加权法、目标规划法、θ-约束法和修正的游戏理论在内的几种多目标优化方法。探讨空间桁架多目标优化设计时各个目标函数的冲突性。采用一个评价函数来评判这些多目标优化方法得到的结果的优劣。修正的游戏理论和目标规划法给出的结果可以认为是在设计目标有冲突时适度平衡的最佳折衷方案。     

不可微多目标规划的基本定理

本文把单目标规划中的Kuhn-Tucker条件,Lagrange鞍点问题和Fritz John(鞍点)条件分别推广到一般多目标规划的情形,讨论了八个问题之间的关系,建立了不可微多目标规划的基本定理。     

A Multiobjective and Stochastic System for Building Maintenance Management

Building maintenance management involves decision making under multiple objectives and uncertainty, in addition to budgetary constraints. This article presents the development of a multiobjective and stochastic optimization system for maintenance management of roofing systems that integrates stochastic condition-assessment and performance-prediction models with a multiobjective optimization approach. The maintenance optimization includes determination of the optimal allocation of funds and prioritization of roofs for maintenance, repair, and replacement that simultaneously satisfy the following conflicting objectives: (1) minimization of maintenance and repair costs, (2) maximization of network performance, and (3) minimization of risk of failure. A product model of the roof system is used to provide the data framework for collecting and processing data. Compromise programming is used to solve this multiobjective optimization problem and provides building managers an effective decision support system that identifies the optimal projects for repair and replacement while it achieves a satisfactory tradeoff between the conflicting objectives.     

Interfacing system dynamics and multiobjective programming for regional water resources planning

A preliminary planning methodology is presented. This approach relies upon a strategy identification mechanism, a system dynamics model to simulate the behaviour of the identified strategies and multiobjective programming formulations to evaluate them. An application to regional water resources planning in the Guadiana basin is included to illustrate the methodology.     

A grey fuzzy multiobjective programming approach for the optimal planning of a reservoir watershed. Part B: Application

The conflict between environmental protection of reservoir water quality and economic development by different uses of land within a watershed is a problem that constantly bothers the public officials in regional planning. Besides, the uncertainties regarding to the fuzzy goals in decision making and the impreciseness of parameter values always create additional difficulties in systems analysis. This paper applies the grey fuzzy multiobjective linear programming (GFMOLP) method for the evaluation of sustainable management strategies for optimal land development in a reservoir watershed. In particular, it demonstrates how uncertain messages in water resources management systems can be quantified by specific fuzzy membership functions and grey numbers in a multiobjective analytical framework. A case study of the planning for land-use programs in the Tweng-Wen reservoir watershed in Taiwan was prepared for the purpose of demonstration.     

Multiobjective optimization of building design using TRNSYS simulations,genetic algorithm,and Artificial Neural Network

Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption.     

A model used in creating a work-rest schedule for laborers

This paper presents a theoretical model that can be used to create work-rest schedules for construction workers. Two objectives are proposed in the optimization processes when such schedules are created. These objectives include minimizing the time for completing jobs and minimizing any extra energy expended by laborers due to inappropriate work assignments that force them to work longer than the maximum acceptable work duration. A GA-based mechanism was developed to search the Pareto front, which is one of the effective ways for solving multiobjective optimization problems. Such a mechanism was implemented by computer programming using a numerical software tool called MATLAB. Theoretical examples demonstrated that the proposed model efficiently created work-rest schedules.     

Aggregate Blending Using Genetic Algorithms

Abstract:   Aggregate blending consists of finding the proportions of fractions to form a final blend satisfying predefined specifications. It is a problem which is posed in many ways, and solved by using different techniques. These techniques range from simple graphical methods to advanced computer methods such as nonlinear programming or dynamic programming. In this article, an aggregate-blending problem is formulated as a multiobjective optimization problem and solved by using genetic algorithms (GAs). It is shown that in this way all existing formulations of an aggregate-blending problem can be covered and solved. The effectiveness of this new application is demonstrated through numerical examples. The technique is shown to be quite versatile in tackling multiple objectives including cost minimization, and approaching at best a given target curve. Linear and nonlinear cost functions can be dealt with equal ease; additional objectives may be inserted into the problem with no difficulty. The user has the possibility of defining and finding the best solutions with Pareto optimality considerations.     

试验设计法在硬岩PFC~(3D)模型细观参数标定中的应用

利用试验设计法针对硬质岩体颗粒离散元数值研究中的细观参数标定问题进行了研究。首先利用Plackett-Burman试验设计分析了细观参数对宏观响应的敏感性,并建立了宏观力学指标和细观参数之间的线性关系,然后利用响应曲面法考查了显著影响参数之间的相互作用,得到了宏观响应与细观参数之间的非线性关系。最后将问题转化为非线性多目标数学规划问题利用MATLAB软件中FGOALATTAIN函数进行求解。通过和典型硬岩物理试验结果对比发现,利用试验设计法标定参数建立的颗粒离散元模型可以很好地反映单轴和低围压下岩石的破坏过程,但是由于采用了球形颗粒对于高围压下的拟合效果偏弱。利用PB设计、响应曲面法并结合数学规划建立的细观参数标定方法可以反映各细观参数对宏观力学响应的敏感性并给出明确的函数表达式,同时可以通过增加求解过程中的约束条件来体现更多的岩石力学特性。     

遗传算法在给水管网优化设计中的应用

遗传算法是一种新型的进化算法，它是一种通过模拟自然进化过程搜索最优解的随机寻优的数学规划方法．阐述了应用遗传算法进行给水管网优化设计的原理、特点，并对青岛市城阳区某工业园给水管网工程进行优化求解．