某常减压装置换热网络优化

从整个系统的角度出发对常减压装置换热网络进行优化,在保持换热终温不变的基础上,省出常减压换热网络的工艺物流,用作其他装置热源,进行和其他装置的热联合改进。结果表明,该方法节能效果十分明显,改造后可节约其他装置蒸汽消耗6.0 t/h,且涉及到的调整较小,在工程上更易实现。因此,采用系统分析方法,可以很好地降低全厂的能耗。     

工程建设项目的高维多目标综合优化研究

作为一类复杂优化问题,工程项目的多目标优化具有重要实践价值,因而受到工程管理领域学界和业界的广泛关注。提出的工程项目多目标优化问题旨在根据工期、成本、质量、安全、环保五维目标之间的关联,合理安排工程实施进度计划及项目资金投入,从而实现对五维目标统筹兼顾、综合优化。针对问题具有五维目标这一高维多目标优化特征,设计NSGAIII算法有效求解Pareto 解集,分别采用分层序列法和理想点法、功效系数法从Pareto 集合中选择最优方案,并通过工程案例验证算法的有效性。     

基于遗传算法的飞艇结构多目标优化

首先,根据软式飞艇结构的力学特点建立了飞艇结构的多目标优化模型,以外荷载作用下飞艇“应力最小”、“变形最小”及“自重最小”为优化目标,确定膜材厚度、膜材弹性模量、飞艇内气压及飞艇细长比为优化变量.其次通过加权系数法将多目标优化转化为单目标问题,并采用遗传算法进行求解.最后,采用此方法对某一飞艇结构进行多目标优化.研究结果表明：飞艇的优化模型是有效的;结合加权系数法和遗传算法,可以获得各个目标均较理想的优化结果;通过进行多目标优化可以为飞艇结构的设计提供合理建议.     

基于遗传算法的飞艇结构多目标优化

首先,根据软式飞艇结构的力学特点建立了飞艇结构的多目标优化模型,以外荷载作用下飞艇"应力最小"、"变形最小"及"自重最小"为优化目标,确定膜材厚度、膜材弹性模量、飞艇内气压及飞艇细长比为优化变量。其次通过加权系数法将多目标优化转化为单目标问题,并采用遗传算法进行求解。最后,采用此方法对某一飞艇结构进行多目标优化。研究结果表明:飞艇的优化模型是有效的;结合加权系数法和遗传算法,可以获得各个目标均较理想的优化结果;通过进行多目标优化可以为飞艇结构的设计提供合理建议。     

基于遗传算法质量工期成本多目标优化研究

本文应用遗传算法及系统可靠度理论 ,建立了基于遗传算法的质量、工期、成本的多目标优化模型 ,并编制了相应的程序 ,使建筑工程领域的进度控制、成本控制和质量控制这三大控制目标达到了协调统一     

基于遗传算法的建设工程风险决策多目标优化研究

在建设工程风险决策过程中,只考虑单个目标因素的决策并不能满足实际工程的要求。为了使决策更加科学、合理,在风险识别和风险评价的基础上,综合考虑包括风险管理成本在内的多个目标因素对决策结果的影响,建立了工程风险多目标决策优化模型,通过多目标遗传算法NSGA-Ⅱ,实现了工程风险决策方案的优化。最后,对优化结果的分析和评价,证明了所建立的模型在解决工程风险决策问题上的有效性和实用性。     

基于约束多目标优化算法的盾构隧道设计方法

当前盾构隧道的设计仍主要依托于经验确定设计参数,然后对其安全性进行校核,难以定量的考虑成本和控制指标(收敛变形等)的关系。以衬砌厚度、截面配筋率、横向接头螺栓直径为设计参数,以成本及结构的水平收敛变形为优化目标,结合多目标优化的算法,进行了盾构隧道的横断面设计。采用非支配原理,通过引入约束违反函数,实现了基于NSGA-Ⅱ算法的复杂约束条件处理。最后通过具体算例,完成了满足截面安全性要求的限制条件下,完整Pareto前沿面的获取工作,并与无约束优化结果进行了分析对比,说明了迭代过程的收敛性,阐述了所得Pareto前沿面的价值意义,得到了不同设计条件下的最优解。     

基于BIM与遗传算法的建筑工程施工进度多目标优化研究

为解决施工进度优化领域数据获取、定量分析、模型求解等难题,构建了基于BIM与遗传算法的建筑工程施工进度优化体系.在此基础上,建立了以成本最低和资源均衡为目标的"工期—成本"和"工期固定—资源均衡"施工进度两阶段多目标优化模型,并运用遗传算法进行求解,确定了各工序最优持续时间和非关键工序最优开始时间.在实际工程中对所建体...     

基于快速群搜索算法的双层球面网壳多目标优化

提出一种新的群智能算法,即多目标快速群搜索优化（MQGSO）算法,以双层球面网壳为研究对象,进行结构节点最大位移最小化和结构质量最轻化的多目标优化设计,网壳优化后的Pareto前沿分布均匀且范围较广,分别对多目标优化和单目标优化后的结构进行多维地震作用时程响应分析。结果表明:多目标优化得到的结构优于单目标的优化结果,所提出的多目标快速群搜索算法能很好地实现复杂空间结构的多目标优化设计。     

基于模糊理论的行星传动多目标优化

讨论了基于模糊理论的行星传动多目标优化．在充分考虑各模糊因素的情况下,建立了行星传动的多目标优化数学模型,运用模糊理论和方法求其最优解．     

“七八个星天外”——追念韦尔默教授

<正>从国内的某公众号辗转得知阿尔布莱希特·韦尔默(Albrecht Wellmer)教授仙逝的消息时,我正结束在闵行公寓一天的劳作,从学校北门外喝了两杯德国啤酒于踉跄中回到宿舍。说来也是"冥冥中"有"巧合",我为之伏案一整天的正是主要由自己编译的韦尔默那部文集《后形而上学现代性》中的两篇文字:《现代世界中的自     

索穹顶结构预应力多目标优化的小生境遗传算法

根据索穹顶结构的柔性结构特点和力学特性,探索以质量最小、刚度最大和支座反力最小为目标函数,以预应力整体可行性准则、荷载态应力控制和位移限值为约束条件的多目标优化模型。结合自适应技术、预选择机制和共享函数小生境技术对传统遗传算法进行改进,利用加权系数法将多目标优化问题转换为单目标Pareto最优解问题。利用向量式有限元对不同预应力水平的结构进行静力分析。最后通过两个不同类型索穹顶算例,采用自编程序的计算结果证明所提出的方法是有效的,为索穹顶结构预应力优化分析提供了新的计算方法。     

遗传算法在空间杆系结构优化中的应用

介绍了一种基于遗传算法和满应力准则法的空间杆系结构优化方法，该法综合了满应力准则法的搜索定向性和遗传算法的全局搜索能力，数值计算实例表明该法可以显著提高收敛速度，具有很强的适应性。     

基于遗传算法悬臂式挡土墙优化设计

以挡土墙优化设计为出发点,提出一种可有效克服局部最优解的优化方法——遗传算法,就采用遗传算法进行悬臂式挡土墙优化设计的过程进行了深入探讨,并通过工程实例证明了该方法的有效性。     

Topology optimization method of lattice structures based on a genetic algorithm

A two-stage topology optimization method of lattice structures based on a genetic algorithm is proposed. The first stage is the form-finding analysis of lattice structures, and the optimal initial shape was achieved with the numerical inverse hanging method. The second stage is the topology optimization of single-layer lattice structures, which can be realized by changing the mesh size and the tube configurations to minimize the total weight of steel tubes subject to the design requirements. The mesh configuration optimization is realized through the adjustment of the nodal horizontal co-ordinates and the removal of tubes with lower stress. The maximum displacement of the structure, the maximum stress of the circular steel tubes, and the nonlinear buckling load are the state variables, and a genetic algorithm (GA) is the optimization algorithm. Different stress-limiting values used to delete the tubes were discussed. The numerical examples show that the two-stage topology optimization method for lattice structures proposed in this paper is correct and efficient. Furthermore, the forms of the optimized structure are rich, and the structure is lightweight and efficient.     

基于遗传算法的混凝土泵车臂架铰点系统的优化

针对混凝土泵车臂架间弯板式6铰点连杆机构,建立其铰点位置和油缸力随着臂架展开角变化的数学模型,采用遗传算法对建立的多目标数学模型进行多工况联合优化,避免了以往只优化典型工况的局限性。通过多工况优化,在保证臂架正常展开的同时,改善了变幅油缸和铰点连杆机构的受力性能,并减小了变幅油缸全伸时的长度,从而为臂架铰点系统的设计及变幅油缸的选取提供依据。     

基于遗传算法的张弦桁架设计与选形优化

张弦桁架设计参数较多且相互影响,常伴有较大的几何非线性变形,因此需要精确寻找结构形状及预拉力,以获得结构整体性能的优化结果.在MATLAB平台上利用多种群遗传算法,通过命令流调用SAP2000,在对结构优化的同时,实现"找形"及"找力";第一阶段寻找各类构件的截面,发现桁架弦杆截面的改变对整体优化结果影响较小,在此基础...     

Multi-objective genetic optimization for scheduling a multi-storey building

Multi-objective optimization problems can be found in various fields, however, optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. This paper proposes developed Genetic Algorithms (GAs) that enabled multi-objective optimization for scheduling a multi-storey building. However, a trade-off between time and cost for habitation projects is required to face limited fund and increasing population environment. This problem has an important position for developing countries' governments because it's related to low cost habitation projects. Multi-storey buildings are classified as special repetitive projects because of skeleton constraints. Activities can be classified into: repetitive and non-repetitive ones. The present model enables construction planners to direct controlled time–cost construction plan by investigating optimal plans, generated from a set of feasible alternatives, which minimize project duration, total number of crews, and total work interruptions. This model satisfies the following aspects: 1) it is based on the line of balance technique; 2) it considers non-serial typical activity networks with finish–start relationship and both lag and overlap time between activities are allowed; 3) it utilizes a tool to identify activities in a typical repeated floor; and 4) it is developed as a spreadsheet template that is easy to use. Details of the model with visual charts are presented. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the scheduling of a multi-storey building.     

Multi-objective optimization of cellular fenestration by an evolutionary algorithm

This paper describes the multi-objective optimized design of fenestration that is based on the façade of the building being divided into a number of small regularly spaced cells. The minimization of energy use and capital cost by a multi-objective genetic algorithm was investigated for: two alternative problem encodings (bit-string and integer); the application of constraint functions to control the aspect ratio of the windows; and the seeding of the search with feasible design solutions. It is concluded that the optimization approach is able to find near locally Pareto optimal solutions that have innovative architectural forms. Confidence in the optimality of the solutions was gained through repeated trail optimizations and a local search and sensitivity analysis. It was also concluded that seeding the optimization with feasible solutions was important in obtaining the optimum solutions when the window aspect ratio was constrained.