基于计算智能的岩土力学模型参数反演方法及其工程应用

最优估计的岩土力学模型参数是通过比较现场观测到的信息数据与理论模型得到的模型数据的差异而得到的。通过定义目标函数，将参数识别反问题转化为优化问题处理。基于梯度搜索方法的参数反演方法缺陷在于无法保证搜索到全局最优解，其主要原因在于观测误差和模型误差的存在。Tihonov(1963)证明，如果正问题(Forward Problem)是线性的，那么，反问题的解存在唯一并且连续地依赖于观测数据(稳定)。关于地下水反问题和热传导反问题以及位移反分析的数值试验发现，当正问题是线性时，如果当不考虑观测数据的观测误差时，反问题的解是唯一的，也就是说，目标函数是凸函数，正如Tihonovr所指出的那样；但是，当考虑到观测数据的观测误差时，即使正问题是线性的，反问题的目标函数是非凸的，反问题解是不唯一的。观测误差越大，目标函数的局部极小值数目越多。     

信赖域-共轭梯度法在麦克斯韦方程参数识别中的应用

针对麦克斯韦方程中的电导率参数识别问题,构造出具有全局收敛性的正则化信赖域共轭梯度算法。此参数识别算法充分融合了最优化领域的传统优化方法—共轭梯度法和新型优化方法—信赖域方法以及正则化方法的优点,使得这种算法具有较强的全局搜索能力,能够很好地应用于麦克斯韦方程的参数识别问题。     

基于优化算法的岩体初始应力场随机识别方法

根据隧道开挖后其周围地应力变化的观测数据，建立了基于优化算法的岩体初始应力场识别方法。将地应力场参数识别反问题转化为优化问题，然后采用BFGS方法求解。最优估计的岩体初始应力场是通过比较观测到的应力变化与预计值的差异而得到的，数值算例中考虑了观测误差对参数识别结果的影响。采用所建立的反演方法，地应力场的主应力的大小和方向可以被准确识别出来。数值计算结果表明，所建立的地应力场反演方法是十分有效的，并且具有良好的抗观测噪音的能力。     

模拟退火算法在概率积分法参数反演中的应用

针对传统方法求取概率积分法参数存在易发散且难以获得最优解的不足,本文提出将模拟退火算法应用到反演概率积分法参数中。利用模拟退火算法在搜索解时能够跳出局部最优解,获得全局最优解的特点,建立模拟退火法反演概率积分参数模型,求出概率积分参数。通过理论分析与实验验证,模拟退火算法反演概率积分法参数精度高,并对随机误差和粗差有一定的抗干扰能力,而且实验反演的地表下沉数据基本符合实测数据,获得了较好的结果,证明了该模型的可行性与有效性。     

改进的蚁群算法及其在桁架结构优化中的应用

蚁群算法是优化领域中的一种新型模拟进化算法,具有很强的搜索较优解的能力,其缺点是搜索时间长、容易出现停滞现象。引用局部搜索能力较强的模拟退火算法对其改进,使其跳出局部最优,发现更高质量解。并将其成功应用在25杆桁架中,结果表明,基于模拟退火的改进蚁群算法是有效可行的,是解决组合优化问题的有效方法。     

基于先验信息的供水管网阻力系数识别

供水管网阻力系数识别是指通过调整管网水力模型中管道阻力系数,使模型计算值与监测值相符的过程。由于实际中监测点数量有限,管网阻力系数识别为欠定的优化问题。现行方法通常采用管道分组这一参数化方法将欠定问题转换为超定,应用遗传算法或其它随机搜索算法求解。提出了基于先验信息的供水管网阻力系数识别算法,所提出算法根据管道管材、管龄等先验信息对管道阻力系数进行估计,并将估计值作为伪观测值引入目标函数将欠定优化问题转换为超定,采用高斯-牛顿算法进行求解。与现有方法相比,所提出算法避免了管道分组不唯一的问题;再者,推导了供水管网阻力系数雅克比矩阵解析式用于搜索向量构造,提高了参数识别计算效率。采用小型管网阐明了雅克比矩阵计算及搜索向量构造,利用大型管网验证了算法的实用性。     

基于支持向量机和模拟退火算法的位移反分析

提出了一种基于支持向量机和模拟退火算法的位移反分析方法,一方面用支持向量机代替有限元计算提高计算分析速度,另一方面用模拟退火算法代替传统的优化算法,避免优化过程中目标函数陷入局部极小值而无法继续寻优的状态,从而提高反演的效率精度。应用该方法对边坡的岩体力学参数进行反演,反演结果验证了模型的可行性。     

基于SAGA的工程项目资源均衡优化方法

为提高工程项目资源优化效率,提出了基于模拟退火遗传算法(SAGA)的工程项目资源优化方法。基于对模拟退火算法(SA)和遗传算法(GA)的搜索能力、优化机制、优化结构和优化操作互补性和可融合性的分析,将模拟退火算法优化结果作为遗传算法初始种群,实现两者的有机结合。通过算例对模拟退火遗传算法的优化效率进行验证,结果表明:在工程项目资源均衡优化问题中,遗传算法最优解稳定性受种群大小影响较大,且种群越大,算法优化效率越低;模拟退火算法在最优解稳定性和运行时间方面的表现均欠佳,不能满足实际优化需求;模拟退火遗传算法能够用较小种群,在较短运行时间内得到100%稳定的最优解,在该问题中具有较强的适用性。     

高强混凝土配合比优化设计

论述了遗传算法与模拟退火算法的运算机理，以及遗传算法在全局搜索中的优势和模拟退火算法在局部寻优中的能力，进而提出性能更好地解决优化问题的模拟退火-遗传算法，并提出高强混凝土配合比的优化设计数学模型，指出模拟退火-遗传算法使得高强混凝土配合比的优化设计简捷方便，并能提高经济效益，具有工程实用价值。     

基于MAC准则的模拟退火算法传感器优化布置研究

基于模态置信度MAC准则，通过建立优化约束条件及目标函数，构建完整的优化数学模型，引入模拟退火算法的抽样和退火过程，使系统能量逐渐降低并达到新的平衡点，如此反复获得模型最优解，基此建立传感器优化配置法。通过某拱塔斜拉桥主梁加速度传感器优化布置工程的实例分析，证明基于MAC准则的模拟退火算法在求解传感器优化布置上，具有优良的并行性和搜索全局的功能。     

基于逐个修正法的含水层参数反演

含水层参数的确定,是进行地下水资源科学管理的基础和关键。针对含水层参数反演解往往不唯一的问题,提出采用最优化方法中的逐个修正法,将反问题转化为一系列的正问题进行求解;利用正问题的解是适定的这一性质,克服了反问题不适定问题;最后以无界承压含水层井流模型为例,讨论了逐个修正法在含水层参数反演中的应用,实例分析结果表明,此种方法有很好的稳定性,是一种值得在实际中推广应用的含水层参数反演方法。     

Sequencing and scheduling highway network expansion using a discrete network design model

The motive behind this paper is to produce an NDP model that prescribes the final shape of a transportation network and the sequence and schedule of facility construction during the planning span as well. The proposed bi-level NDP model fills the gap between existing NDP models and practitioners’ needs because, in practice, planners have to select investment projects on a year-by-year basis. Conversely, existing models suggest only the optimal network configuration for a planning horizon. A genetic algorithm and a simulated annealing algorithm are proposed along with an exhaustive search algorithm as solution algorithms. Testing these algorithms with an example problem revealed that the simulated annealing worked superiorly to the genetic algorithm. The paper also demonstrates that the model is applicable to a real world problem by showing that the computational time needed to solve the example problem is not prohibitively large.     

Simulation‐based dynamic traffic assignment: Meta‐heuristic solution methods with parallel computing

The aim of this study is to solve the large‐scale dynamic traffic assignment (DTA) model using a simulation‐based framework, which is computationally a challenging problem. Many studies have been performed on developing an efficient algorithm to solve DTA. Most of the existing algorithms are based on path‐swapping descent direction methods. From the computational standpoint, the main drawback of these methods is that they cannot be parallelized. This is because the existing algorithms need to know the results of the last iteration to determine the next best path flow for the next iteration. Thus, their performance depends on the single initial or intermediate solution, which means they exploit a solution that satisfies the equilibrium conditions more than explore the solution space for the optimal solution. More specifically, the goal of this study is to overcome the drawbacks of serial algorithms by using meta‐heuristic algorithms known to be parallelizable and that have never been applied to the simulation‐based DTA problem. This study proposes two new solution methods: a new extension of the simulated annealing and an adapted genetic algorithm. With parallel simulation, the algorithm runs more simulations in comparison with existing methods, but the algorithm explores the solution space better and therefore obtains better solutions in terms of closeness to the optimal solution and computation time compared to classical methods.     

Hybrid Meta-Heuristic Algorithm for the Simultaneous Optimization of the O–D Trip Matrix Estimation

Abstract:   In the present article, the origin–destination (O–D) trip matrix estimation is formulated as a simultaneous optimization problem and is resolved by employing three different meta-heuristic optimization algorithms. These include a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a hybrid algorithm (GASA) based on the combination of GA and SA. The computational performance of the three algorithms is evaluated and compared by implementing them on a realistic urban road network. The results of the simulation tests demonstrate that SA and GASA produce a more accurate final solution than GA, whereas GASA shows a superior convergence rate, that is, faster improvement from the initial solution, in comparison to SA and GA. In addition, GASA produces a final solution that is more robust and less dependent on the initial demand pattern, in comparison to that obtained from a greedy search algorithm.     

Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints

Since scheduling of multiple projects is a complex and time-consuming task, a large number of heuristic rules have been proposed by researchers for such problems. However, each of these rules is usually appropriate for only one specific type of problem. In view of this, a hybrid of genetic algorithm and simulated annealing (GA-SA Hybrid) is proposed in this paper for generic multi-project scheduling problems with multiple resource constraints. The proposed GA-SA Hybrid is compared to the modified simulated annealing method (MSA), which is more powerful than genetic algorithm (GA) and simulated annealing (SA). As both GA and SA are generic search methods, the GA-SA Hybrid is also a generic search method. The random-search feature of GA, SA and GA-SA Hybrid makes them applicable to almost all kinds of optimization problems. In general, these methods are more effective than most heuristic rules. Three test projects and three real projects are presented to show the advantage of the proposed GA-SA Hybrid method. It can be seen that GA-SA Hybrid has better performance than GA, SA, MSA, and some most popular heuristic methods.     

遗传算法在水文地质参数反演中的应用

本文基于遗传算法 (GA)这一全局优化技术 ,以及地下水模拟的有限元模型 ,给出了地下水系统的反演方法。并应用到北京市应急水源地水文地质参数反演中 ,反演参数包括渗透系数和给水度 ,共 8个分区的 2 4个变量 ,来验证模拟模型的可用性、适用性和鲁棒性。结果表明 ,一般情况下 ,遗传算法可以得到比较满意的结果 ,考虑测量误差和减少观测孔个数的情况下也可以得到与“标准”值相近似的解。通过优化各个变量区间的加速遗传算法可以提高算法的收敛速度。GA在实际参数获得比较困难的情况下可成功用在区域含水层反演模拟中     

综合改进的遗传算法反演三维地下水流模型参数

在简单的遗传算法的基础上，提出了一种综合改进的遗传算法，在反演地下水水流参数时，具有收敛速度快、解的精度高和避免出现早熟等优点。以非均质各向同性承压三维非稳定流动为理想模型，结合有限元法讨论了用遗传算法反演水文地质参数的过程。综合改进的遗传算法非常有效，在地下水渗流和水资源评价计算中有广阔的应用前景。     

基于模拟退火差异演化算法的资源约束项目调度问题研究

资源约束项目调度问题是工程管理领域研究的热点之一,但无论是模型构建还是求解均有一定的难度,尤其是模型求解已被证明是NP-hard 问题。鉴于此,构建了以工期最短为优化目标的项目调度模型,为便于求解,将模型的显性约束和隐性约束做了适当处理,并利用差异演化算法较强的记忆能力和全局收敛能力以及模拟退火的局部跳出能力,将模拟退火算法和差异演化算法进行有效结合。通过工程实例,分别采用遗传算法、差异演化算法以及模拟退火差异演化算法进行求解。结果表明,3 种算法都可以收敛到最优解,但论文算法具有较大的搜素范围与局部寻优能力,同时求解的稳定性指标明显优于遗传算法和差异演化算法。     

Comparison of algorithms for seismic topology optimisation of lifeline networks

A performance comparison among five optimisation algorithms for the topology design of lifeline network subjected to earthquakes is presented in this study. The topology optimisation model in conjunction with the argument of seismic reliability is firstly introduced for the design of lifeline networks subjected to earthquakes. Subsequently, two quite standard optimisers used in the numerical optimisation, i.e. a genetic algorithm (GA) and a simulated annealing algorithm, are investigated. Their hybrid scheme, entitled a simulated annealing GA that combines the advantages of two standard optimisers, is introduced as well. Besides, an ant colony algorithm and a particle swarm algorithm that have been developed in recent years are explored. Three modelled lifeline networks, including two benchmark networks and one actual network, are used as the numerical carriers that the five optimisation algorithms accommodate. It is concluded that the simulated annealing GA provides an excellent tool with higher efficiency to achieve optimal topology of lifeline networks.