利用改进的和声搜索算法对组合楼板体系的成本优化

利用改进的和声搜索算法对组合楼板体系进行了成本优化计算。这个算法是利用在音乐中寻找最好和声效果的方法而提出的,类似于最优化设计中寻找最优解的过程。利用LRFD-AISC法设计了组合楼板体系,单元中包括钢筋混凝土楼板和钢梁,将结构成本作为目标函数(因为其受承载能力和强度的影响较小)。通过对组合楼板体系的算例分析验证了所提方法的有效性。     

改进和声搜索算法及其在土坡稳定分析中的应用

土坡非圆临界滑动面的寻求是土坡稳定分析的重要步骤,它是一个多极值的优化问题。利用改进的和声搜索算法和修复策略来寻求复杂土坡的临界滑动面及其对应的安全系数,并对改进和声搜索算法与基本和声搜索算法的结果进行了比较,发现改进和声搜索算法比基本和声搜索算法能搜索到更危险的滑动面,原因在于,改进和声搜索算法在每次迭代步中产生多个新解,而基本和声搜索算法中每次迭代产生一个新解。此外,修复策略比惩罚策略更能有效地搜索解空间。结果表明,改进的和声搜索算法和修复策略可以用来进行土坡稳定分析。     

基于改进麻雀搜索算法的疏散路径规划

建筑物内部发生火灾时环境复杂多变,传统疏散指示路径难以根据实际火场情况进行有效的路径规划,为此引入一种改进麻雀搜索算法。首先,根据实际火源位置设置麻雀算法预警值参数,实现算法路径规划过程中对于火源等危险区域的躲避。其次,对麻雀搜索算法位置更新公式进行优化,引入精英反向学习策略以及带有动态权重系数的正弦余弦优化算法,进一步针对麻雀搜索算法易于陷入局部最优解的问题进行改进。最后,采用栅格法搭建3种地图类型,将改进麻雀搜索算法与基本麻雀搜索算法、灰狼算法进行路径规划对比,得出该改进麻雀搜索算法在火灾复杂环境下有较好的危险区域躲避能力以及路径规划能力,在路径长度、拐点个数方面优于另外两种对比算法,在搜寻时间方面稍有不足。     

基于NSGA-Ⅱ的工程项目工期-成本-质量均衡优化

工程项目工期-成本-质量多目标均衡优化是近年来工程项目管理领域的研究热点问题。针对这一问题,分析了工期与质量之间的关系,提出了工期与质量的s-型曲线,构建了工期一成本一质量的均衡优化模型,通过经典多目标测试函数对NSGA-II求解多目标优化问题的可行性和有效性进行了测试。在此基础上,利用该算法进行了工程项目工期-成本-质量的多目标均衡优化,得到了一组Pareto最优解,为项目管理者的决策提供了依据。     

基于粒子群算法的工程项目多目标优化问题研究

为了更有效地解决工程项目的工期—成本—质量优化问题,从施工单位的角度出发,基于工期、质量、成本间的对立统一关系,以双代号网络图中每项工作的持续时间为自变量,建立工期—成本—质量优化模型,采用标准粒子群算法来优化求解。为了消除量纲对评价标准的影响,对 3 个目标适应值采取了标准化的处理方法。利用 Matlab 软件,对一个工程案例进行多目标优化,通过与蒙特卡罗方法进行对比,分析了粒子群算法的计算效率,优化结果验证了粒子群算法求解工程项目多目标优化模型的可行性和适用性。     

基于BIM–遗传算法的建筑施工期多目标优化设计

本文以遗传算法为基础,结合BIM新技术,构建了兼具优化与仿真模拟功能的寻优模型,对建筑项目施工期的工期–成本进行优化分析。该模型分析了工期和成本变化的特点,修正了工期–成本关系曲线;改进设计了遗传算法中的遗传机制与数据处理方法,考虑建筑项目的规划及生产特点,重新布置了优化模型流程;将算法与BIM平台对接,引入了虚拟施工与碰撞检查等概念与方法,并给出了可行的对接操作方法。实验结果证明,这种新方法用于处理复杂的多目标优化问题是合理、有效的,不仅可以根据之前的工期/成本或合同数据,快速提供最优的工期/成本决策,还可以可视化施工过程,最终给出可靠的施工进度动态安排。优化框架对其他多目标问题也有参考价值。     

基于改进遗传算法的工程施工进度优化

项目管理者可以通过活动搭接来优化项目工期。针对项目搭接中进度-成本优化的问题,本文引入精确化思想,构建了工程项目进度搭接策略决策模型,通过优化算法输出具备精确搭接率的搭接策略,在满足工期要求的前提下得出最低成本。将遗传算法与精确搭接的理念及设计结构矩阵的理念结合,并通过对染色体编码方式的变化改进遗传算法,得到最优策略。本文通过搭接策略决策模型和改进遗传算法可以得到具备精确搭接率的最优进度方案,为进度和成本均衡要求下的项目进度安排策略提供了科学的定量化参考。     

工程项目工期--成本综合模糊优化

针对工程项目中工期的不确定性,本文提出了一种综合模糊集合论与遗传算法的新方法来解决工期—— 成本优化问题。该算法能较好的寻找项目在不同风险水平下的工期——成本优化的最优解,同时能深入分析工期 和成本的理想平衡点,为决策者提供相关信息,用于指导实践。     

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。     

基于微粒群算法的建筑工程成本优化技术研究

在保证建筑工程进度的前提下,如何尽可能地减少施工总成本,是包括施工企业在内的相关同业普遍关注的一个问题。因此,针对建筑工程总费用的最小化问题,提出一种基于微粒群算法的BIM建筑工程成本优化技术:首先,建立工程项目"成本—工期"均衡优化数学模型,以此为基础,建立基于BIM的建筑工程成本优化信息模型,然后采用改进的微粒群算法优化求解上述数学模型。最后,以实际建筑工程项目为例,通过所提方法对施工进度计划进行优化,优化结果验证了所提方法的有效性。     

Stochastic time-cost optimization using non-dominated archiving ant colony approach

This article employs a non-dominated archiving ant colony approach to solve the stochastic time-cost trade-off optimization problem. The model searches for non-dominated solutions considering total duration and total cost of the project as two objectives. In order to expect more realistic outcomes for the time-cost trade-off problem, uncertainties in time and cost of the project should be taken into account. Fuzzy sets theory is used to answer for uncertainties in time and cost of the project. The model embeds the α-cut approach to account for accepted risk level of the project manager. Left and right dominance ranking method is used for finding non-dominated solutions. The ranking method employs decision maker's optimism using β concept. The performance of the model is tested according to performance metrics for multi-objective evolutionary algorithms proposed in the literature. The results show that the algorithm is adequately reliable. A case study is solved to show the application of the proposed model for the uncertain time-cost trade-off problem.     

多项目无节奏流水施工时间成本置换优化分析

无节奏流水施工是施工组织的常见形式。文章考虑了多项目环境下多模式的无节奏流水施工组织,并结合传统的time-cost置换问题进行了建模求解。目标函数直接采用双目标形式,并基于NSGA-Ⅱ非劣排序思想进行了算法设计。仿真测试表明,改进的算法能够有效的获得较优的离散time-cost曲线,可以为复杂环境下的无节奏流水施工组织与实施提供决策支持。     

A TIME-COST TRADE-OFF MODEL WITH RESOURCE CONSIDERATION USING GENETIC ALGORITHM

A genetic algorithm (GA)-based model to deal with time-cost trade-off problems is presented. The traditional algorithms assume the unlimited availability of resources. Instead, the proposed model allows for resource constraints. Accordingly, the trade-off is considered in terms of the level of resources to be deployed for each activity. At the same time the model schedules the starts of the activities in order to optimize the objective function. The activity starts is a significant factor in the case of non-uniform available profile. The GA searches both spaces of resource utilization and activity starts to determine the optimal schedule that conforms to the resource available profile. Although, in principle, this has the potential of a combinatorial explosion, earlier work suggests that the GA-based model can be applied to larger networks without appearing to suffer from this problem. Allowing the project manager to think in terms of resource utilization makes this approach consistent with resource allocation problems, and more pragmatic and appealing. The model can also be used to solve conventional time-cost trade-off problems by a simple modification of the objective function. Numerical examples are used to illustrate the working of the model. The examples include a case of varying resource available profile.     

Optimization of time and cost

The most significant decisions relating to the planning and control of projects concern the trade-off between the time and cost parameters. Existing exact models have proved difficult or virtually impossible to implement owing to the large number of variables and constraints involved and to the difficulty of the mathematical analysis. The paper presents an integer linear programming model based on points of breakthrough on the time-cost curve. This approach results in a reduction in the number of variables since the number of breakthrough points is less than the number of points where there is a discontinuity in the time-cost slope. The model is implemented using a program called Trans, which schedules the problem in such a way that it is suitable for the application of a standard integer linear programming package. Trans eliminates the necessity for any mathematical analysis in the formulation of the problem.     

A two-phase GA model for resource-constrained project scheduling

In construction scheduling, problems can arise when each activity could start at different time points and the resources needed by the activities are limited. Moreover, activities have required conditions to be met, such as precedence relationships, resource requirements, etc. To resolve these problems, a two-phase GA (genetic algorithm) model is proposed in this paper, in which both the effects of time-cost trade-off and resource scheduling are taken into account. A GA-based time-cost trade-off analysis is adopted to select the execution mode of each activity through the balance of time and cost, followed by utilization of a GA-based resource scheduling method to generate a feasible schedule which may satisfy all the project constraints. Finally, the model is demonstrated using an example project and a real project.     

基于遗传算法的建筑工程三大目标综合优化

工期、成本和质量是建筑工程的三大主要目标,他们能否顺利实现将直接影响项目总体目标的好坏,而以往提出的模型大都是侧重于对工期一成本两目标的综合优化,很少涉及质量目标,这显然是满足不了现实要求的。因此在这里提出了一个对三大目标综合优化的模型,它是在工期一成本、工期一质量为线性关系的基础上建立的,并且以综合目标函数作为适应度函数建立的依据,运用遗传算法进行求解。最后用一个实例验证了此模型的可行性,并且运算之后可以得到多组解,决策者可以根据自己的偏好选择自己满意的一组解。     

基于和声策略的粒子群优化算法在土坡稳定分析中的应用

土坡极限平衡稳定分析中临界滑动面的搜索是一个复杂的优化问题,在应用常规微粒群算法搜索时往往因参数较多且难以确定以及飞行速度越界的缺陷而陷入局部最优。基于对常规微粒群算法寻优思想的分析,借鉴和声算法的搜索策略来更新粒子的位置,提出基于和声策略的微粒群优化算法,该方法继承了常规微粒群算法中利用本身经验和社会认知的优势,又借鉴了和声策略的简单易行优势。将该方法应用于土坡稳定分析中,通过算例比较分析,证明新算法的有效性。     

Improved genetic algorithm and its application to determination of critical slip surface with arbitrary shape in soil slope

In order to overcome the problem of being trapped by the local minima encountered in applying the simple genetic algorithm (GA) to search the critical slip surface of the slope, an improved procedure based on the harmony search algorithm is proposed. In the searching computation, the new solutions are obtained from the whole information of the current generation. The proposed method may be applied to calculate the minimum factors of safety of two complicated soil slopes. Comparison of the results with existing examples given by other authors has shown that the proposed method is feasible for stability analysis of soil slopes.     

Cost optimization of a composite floor system using an improved harmony search algorithm

In this study, cost optimization of a composite floor system is performed utilizing the harmony search algorithm and an improved harmony search algorithm. These algorithms imitate the musical performance process that takes place when a musician searches for a better state of harmony, similar to the optimum design process which looks for the optimum solution. A composite floor system is designed by the LRFD-AISC method, using a unit consisting of a reinforced concrete slab and steel beams. The objective function is considered as the cost of the structure, which is minimized subjected to serviceability and strength requirements. Examples of composite floor systems are presented to illustrate the performance of the presented algorithms.