层状土中土钉支护结构整体稳定性分析方法

根据极限平衡法的原理,采用简单条分法建立了非均质层状土中的土钉支护结构整体稳定性的分析模型,推导了安全系数的理论表达式;为了克服传统的遗传算法在迭代过程中出现的适应度值标定方式复杂、过早的收敛到局部最优解和在最优值附近收敛速度慢等缺点,提出了采用动态自适应技术和非标准的遗传操作算子改进遗传算法的新算法,并将其引入到土钉支护结构整体稳定性分析中去,建立了一种能同时确定土钉支护最危险滑动面和最小安全系数的动态自适应遗传算法(DAGA)。工程实例分析表明,采用动态自适应遗传算法进行优化,其分析效率更高,收敛速度     

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.     

滑坡非线性演化行为的自组织进化识别

滑坡行为表现出复杂的非线性演化特征,位移是滑坡演化过程中所反馈出的重要信息之一。引入进化算法的全局优化思想,结合时间序列分析基本理论,以斜坡位移时间序列为基础,将遗传规划和遗传算法有机结合在一起,设计了一种模型结构和参数分别进化、共同识别的进化方案,实现对斜坡演化的非线性动力学模型结构和参数的全局最优识别。以新滩及八尺门滑坡为例对滑坡的发展孕育过程进行分析,结果表明,新方法识别获得的非线性动力学模型预测效果较理想,而且表现出较高的自组织进化识别能力。     

岩体随机不连续面产状数据划分方法研究

动态聚类算法从本质上讲是单目标组合优化算法,一般需要事先给定目标分类数和初始聚类中心,且初始聚类中心的选择对数据划分结果影响较大。为了解决该问题,提出将产状数据的划分问题转化为多目标优化问题,并采用小生境Pareto遗传算法进行求解。针对聚类问题的特殊性,采用基于链表的编码方案,并建议相应的遗传操作算子;通过引入小生境技术和Pareto支配集理论,仅通过一次求解可由Pareto支配集给出对应于不同目标组数的最优分组结果,而且不用事先给定目标组数以及初始聚类中心。最后,将算法应用于三峡船闸高边坡岩体实测不连续面产状数据的划分,得到较为符合实际的优势结构面分组。     

A hybrid decision support system for sustainable office building renovation and energy performance improvement

Energy consumption of buildings accounts for around 20-40% of all energy consumed in advanced countries. Over the last decade, more and more global organizations are investing significant resources to create sustainably built environments, emphasizing sustainable building renovation processes to reduce energy consumption and carbon dioxide emissions. This study develops an integrated decision support system to assess existing office building conditions and to recommend an optimal set of sustainable renovation actions, considering trade-offs between renovation cost, improved building quality, and environmental impacts. A hybrid approach that combines A* graph search algorithm with genetic algorithms (GA) is used to analyze all possible renovation actions and their trade-offs to develop the optimal solution. A two-stage system validation is performed to demonstrate the practical application of the hybrid approach: zero-one goal programming (ZOGP) and genetic algorithms are adopted to validate the effectiveness of the algorithm. A real-world renovation project is introduced to validate differences in energy performance projected for the renovation solution suggested by the system. The results reveal that the proposed hybrid system is more computationally effective than either ZOGP or GA alone. The system's suggested renovation actions would provide substantial energy performance improvements to the real project if implemented.     

Fitness-oriented multi-objective optimisation for infrastructures rehabilitations

A new multi-objective decision support system (MODSS) is developed for rehabilitation planning of public infrastructures. The method is generic and provides decision-makers a set of optimal rehabilitation tradeoffs over a desired analysis period. Two main objective functions are handled simultaneously, namely cost and performance, in addition to a set of bounding constraints. The method is based on a new fitness-oriented technique where problem knowledge is taken into account. In order to analyse cost and performance together, a normalisation technique of both objectives is achieved through an innovative time-value concept for both cost and condition states. The proposed method is based on life-cycle costing (LCC) methodology using a dynamic Markov chain to represent the deterioration mechanism and genetic algorithm is used to find the optimal rehabilitation profile. A case study is presented with a comparison between the traditional Markov decision process (MDP) and the newly developed method. The MODSS results in a lower LCC and is found practical in providing a complete maintenance and rehabilitation plan over a required study period. It is proven that the developed multi-objective optimisation is an effective tool in analysing real-life situations involving conflicting goals. Also, weighted sum method could be easily implemented and its outcome is sufficient given that many external factors might alter the decision-makers choice irrespectively of the optimisation method that is used. Furthermore, genetic algorithm is proven useful in the optimisation process in overcoming the computational difficulties associated with large combinatorial problems. The new method is beneficial to researchers and practitioners as it provides a major step towards a broad infrastructure management system.     

Using Genetic Algorithms to Optimize Stopping Patterns for Passenger Rail Transportation

In a passenger railroad system, the stopping pattern optimization problem determines the train stopping strategy, taking into consideration multiple train classes, station types, and customer origin‐destination (OD) demand, to maximize the profit made by a rail company. The stopping pattern is traditionally decided by rule of thumb, an approach that leaves much room for improvement. In this article, we propose an integer program for this problem and provide a systematic approach to determining the optimal train stopping pattern for a rail company. Commonly used commercial optimization packages cannot solve this complex problem efficiently, especially when problems of realistic size need to be solved. Therefore, we develop two genetic algorithms, namely binary‐coded genetic algorithm (BGA) and integer‐coded genetic algorithm (IGA). In many of the past evolutionary programming studies, the chromosome was coded using the binary alphabet as BGA. The encoding and genetic operators of BGA are straightforward and relatively easy to implement. However, we show that it is difficult for the BGA to converge to feasible solutions for the stopping pattern optimization problem due to the complex solution space. Therefore, we propose an IGA with new encoding mechanism and genetic operators. Numerical results show that the proposed IGA can solve real‐world problems that are beyond the reach of commonly used optimization packages.     

基于自适应动态惩罚遗传算法的桥梁监测无线测点优化研究

针对桥梁监测的无线测点优化布置问题,提出一种基于自适应动态惩罚函数的改进广义遗传算法。首先针对无线传感器数量固定和通信距离有限的典型特征将桥梁监测无线测点优化布置表达为约束优化问题,无线传感器的数量和极限传输距离作为优化问题的约束;其次构建了一种能够根据解的偏离程度和种群中高适应度个体数量自动调整惩罚力度的自适应动态惩罚函数;然后采用精英保存机制和末位淘汰策略对基于二重结构编码的广义遗传算法进行了改进;最后利用一大跨悬索桥对该方法进行了验证,并进一步讨论了自适应动态惩罚函数对解的有效性和收敛速度的影响。结果表明:提出的自适应动态惩罚函数能够根据种群的特征自动改变惩罚尺度,保证无线传感器之间的距离小于极限通信距离,同时将无线数据传输距离对桥梁监测信息获取的影响降到最低;改进的广义遗传算法具有很强的全局快速寻优能力,能够快速搜索到全局最优解,优化结果不仅能够满足无线传感网络数据传输距离的要求,还能最大化无线测点的信息获取能力。     

Applications of dynamic programming in survey engineering

Recent research has shown that probabilistic and non-probabilistic dynamic programming techniques can be applied beneficially in the solution of optimal design and management problems in surveying. The survey design system is presented. A non-probabilistic dynamic programming solution of a second order, one dimensional optimal survey design problem is outlined and some applications of Bayesian dynamic programming to survey logistics and management problems are demonstrated. A useful 'technology transfer' technique is included to illustrate one method of introducing systems theory and operations research to survey engineering.     

基于遗传算法的冷弯薄壁檩条优化

利用浮点编码遗传算法与有限条法相结合,以考虑了屋面板约束作用的冷弯薄壁檩条为例,把截面尺寸作为设计变量,使满足各种约束条件下的结构重量达到最优。这种方法不仅改变了传统的设计观念,而且可以丰富冷弯薄壁结构优化设计理论和方法。同时编制了分析程序来进行檩条优化设计。     

基于改进动态规划算法的堆积体边坡稳定性分析

引入图论的概念,将堆积体边坡稳定性分析中搜索最危险滑动面及计算安全系数问题转化为图论中求最短路问题。对动态规划算法进行改进,给出改进动态规划算法的基本方程,利用改进动态规划算法解决图论中求最短路问题,建立基于改进动态规划算法的极限平衡有限元方法。通过一个算例对该方法进行验证,计算结果与推荐答案基本一致,说明该方法适用于堆积体边坡稳定性分析。将该方法用于溪洛渡水电站左岸谷肩堆积体边坡稳定性分析,研究表明,计算得到的最危险滑动面和安全系数与极限平衡法的计算结果基本一致,工程应用效果良好。     

A Dual Variable Approximation‐Based Descent Method for a Bi‐level Continuous Dynamic Network Design Problem

Abstract: The transportation network design problem (NDP) considers modifying network topology or parameters, such as capacity, to optimize system performance by taking into account the selfish routing behavior of road users. The nature of the problem naturally lends itself to a bi‐level formulation of a problem that represents a static case of a Stackelberg game. The NDP is complex because users’ individual objectives do not necessarily align with system‐wide objectives; thus, it is difficult to determine the optimal allocation of limited resources. To solve the bi‐level dynamic NDP, this study develops a dual variable approximation‐based heuristic, which identifies the system‐wide gradient as a descent direction, and designs an iterative solution framework. Descent direction‐based approaches designed to solve bi‐level programming problems typically suffer from non‐differentiability, which can hamper the solution process. The proposed method addresses this issue by approximating the descent direction with dual variables that correspond to cell transmission model constraints and using the constructed rational direction to iteratively decrease the upper‐level objective while maintaining the feasibility of the lower‐level program. The proposed method was empirically applied to three networks of various sizes. The results obtained from this empirical solution were compared with the results from an exact Kth‐best algorithm and a genetic algorithm. The promising results demonstrate the efficacy and efficiency of the proposed descent method.     

Structural optimization using interior penalty function

Mathematical programming methods are among the most powerful optimization techniques. They may be classified into direct or indirect methods. In the indirect methods, the constrained design problem is converted into a sequence of unconstrained problems using penalty functions. In this way, the optimal solution of a constrained problem may be obtained using one of the unconstrained search techniques. The interior penalty function appears to be the most reliable uncon strained method while the variable metric method seems to be an extremely powerful algorithm. This paper presents the use of the interior penalty function coupled with the variable metric method for the solution of structural design optimization problems.     

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．     

基于动态分析的房地产开发投资组合决策规划模型研究

运用数学规划法解决房地产开发投资中的产品组合决策问题具有非常重要的实践意义,但现有的传统规划决策模型在设计中只单一地把收益最大化作为目标函数,并未充分结合市场的实际需求及考虑产品的销售情况,导致投资的产品组合未必能获得最优的投资收益。基于动态分析的房地产投资产品组合决策模型在传统模型的基础上考虑了投资资金的时间价值和市场消化产品的时间要求,从而解决了无法获得最优解的问题。动态分析投资组合决策模型在实践应用中也可以通过非线性规划软件lingo 进行求解。     

混凝土泵车布料机构运动学分析及轨迹规划控制建模与仿真

本文给出了以混凝土泵车各臂油缸长度为参变量的布料机构浇筑过程的轨迹规划计算方法 ,在解决布料机构运动学分析的逆问题时 ,采用了基于多峰值并行搜索的遗传算法来求解最优控制优化目标函数 ,并对施工过程进行了仿真     

基于准则法的建筑结构优化设计

充分考虑建筑结构的特点,基于我国现行建筑结构规范相关条款提出了适合梁、柱的高校优化设计准则,分别以柱轴压比和梁配筋率为指标构造迭代格式。并通过动态编程技术在大型有限元软件ANSYS上进行了程序实现,对一个30层的结构进行了优化设计,结果表明,该方法是有效的可行的。     

含有正转移费用的离散序列搜索

研究了一类带有转移费用的离散序列搜索问题．利用动态规划的原理，将模型转化成一系列循环搜索过程，以求得最优搜索策略．并证明了最优ｕ．ｐ策略存在的充要条件     

裂隙岩体渗流场与应力场动态全耦合参数反演

 考虑到裂隙岩体渗流和力学行为的发生与发展往往是一个动态过程,同时现场观测资料也是一个动态的时间序列,为能及时利用现场量测的新增信息使参数反演更为合理,基于求解非恒定渗流场与弹性位移场动态全耦合正分析理论与方法,应用建立的混合遗传算法作为优化算法,同时利用水头、位移等多类型动态观测资料,建立了裂隙岩体渗流场与应力场动态全耦合的参数反演思路。为避免在耦合反问题中由于利用多类型量测资料所带来的量纲问题,采用了各时刻水头、位移的相对值来构造量纲一的目标函数。待反演参数同时考虑了力学参数与渗流参数两种类型,包括岩块的弹性模量、各组裂隙的切向与法向刚度系数、各组裂隙的初始等效渗透系数等。最后以一简单裂隙岸坡为算例,针对库水位快速上涨情况,以各时刻的动态全耦合正分析结果作为“假想”的实测数据,进行动态全耦合参数反演。反演结果表明,利用不断新增的实测资料可提高反演精度,最终获得的参数反演解与理论解吻合很好。     

An optimal control algorithm for borehole thermal energy storage systems

The aim of this paper is to present an optimal control algorithm to manage borehole thermal energy storage systems (BTES). Such a system gets exhausted, if it is employed intensively, i.e. outlet fluid is outside acceptable temperature ranges, and can no longer provide the desired heat or cold. To avoid this problem a control algorithm is proposed, which simultaneously optimizes the operation costs. This algorithm is based on a dynamic programming technique and results in an array which provides the optimal heat flux for a given field temperature, date and demand. The controller is illustrated on a simulation of an existing building. Several weather scenarios have been examined and the system remained robust under all situations.