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。     

Optimization of steel floor systems using particle swarm optimization

Particle swarm optimization (PSO) is used for the design of composite and non-composite steel floor systems. The design problem is the minimization of the mass or the cost of a steel floor configuration subject to constraints related to the Canadian S16 design standard. The PSO algorithm was applied to three different steel floor bays. Outputs returned are the girder and beams sizes, steel deck profile, concrete slab thickness, number of interior beams and the number of steel studs needed per beam. Results show the PSO can consistently find the optimum floor configuration by minimizing the total mass or cost while satisfying all design criteria.     

A novel approach for optimal chiller loading using particle swarm optimization

This study employs two new methods to solve optimal chiller loading (OCL) problem. These methods are continuous genetic algorithm (GA) and particle swarm optimization (PSO). Because of continuous nature of variables in OCL problem, continuous GA and PSO easily overcome deficiencies in other conventional optimization methods. Partial load ratio (PLR) of the chiller is chosen as the variable to be optimized and consumption power of the chiller is considered as fitness function. Both of these methods find the optimal solution while the equality constraint is exactly satisfied. Some of the major advantages of proposed approaches over other conventional methods can be mentioned as fast convergence, escaping from getting into local optima, simple implementation as well as independency of the solution from the problem. Abilities of proposed methods are examined with reference to an example system. To demonstrate these abilities, results are compared with binary genetic algorithm method. The proposed approaches can be perfectly applied to air-conditioning systems.     

微粒群优化算法在Theis公式参数识别中的应用

采用一种微粒群优化算法来识别承压完整井非稳定地下水运动Theis公式中的水文地质参数。微粒群算法是一种新型的群体智能算法,它将每个个体看作在多维搜索空间中的一个没有重量和体积的微粒,并在搜索空间中以一定的速度飞行,该飞行速度由个体的飞行经验和群体的飞行经验进行动态调整。然后根据个体适应值大小运算,根据适应度函数对微粒的速度和位置进行进化,最终得到足够好的适应度值。本文采用微粒群算法可根据抽水试验资料快速反演Theis公式近似解析解中的水文地质参数。实例计算结果表明该微粒群算法计算速度快,在水文地质逆问题求解中值得推广应用。     

Seismic design of water distribution networks using auto-generation techniques

A feasible approach that uses an auto-generation technique is proposed to design and retrofit water distribution networks (WDNs) subjected to earthquakes. Because pipelines are laid along roads, the road information is extracted and then integrated into the auto-generation technique whereby the rules relevant to laying the pipelines are established. An optimisation model with parameters of network topology and pipeline diameter is developed to design and retrofit WDNs, whose seismic functional reliability index serves as a constraint. An intelligent optimisation program via a particle swarm algorithm is employed to generate the optimal network after a number of evaluations and evolutions. For illustrative purposes, a hypothetical and a real WDN model are investigated, respectively. Numerical results indicate that the optimisation and design method developed in this paper provides a new perspective towards the rational balance between risk and investment.     

基于机器语言的建筑混凝土设计

建筑混凝土的强度受多种因素的影响,其强度的预测是一个多指标综合复杂问题。基于机器算法支持向量机建立了建筑混凝土的强度设计与预测的支持向量机模型,其中模型参数通过粒子群算法进行选择和优化。将建立的模型计算结果与实测混凝土28 d抗压强度进行比较,讨论了各因素与强度值之间的关系。研究表明:预测结果与实测结果一致,可见该模型可以很好的为混凝土设计提供依据。     

Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm

The response of rocks to stress can be highly non-linear, so sometimes it is difficult to establish a suitable constitutive model using traditional mechanics methods. It is appropriate, therefore, to consider modeling methods developed in other fields in order to provide adequate models for rock behavior, and this particularly applies to the time-dependent behavior of rock. Accordingly, a new system identification method, based on a hybrid genetic programming with the improved particle swarm optimization (PSO) algorithm, for the simultaneous establishment of a visco-elastic rock material model structure and the related parameters is proposed. The method searches for the optimal model, not among several known models as in previous methods proposed in the literatures, but in the whole model space made up of elastic and viscous elementary components. Genetic programming is used for exploring the model's structure and the modified PSO is used to identify parameters (coefficients) in the provisional model. The evolution of the provisional models (individuals) is driven by the fitness based on the residual sum of squares of the behavior predicted by the model and the actual behavior of the rock given by a set of mechanical experiments. Using this proposed algorithm, visco-elastic models for the celadon argillaceous rock and fuchsia argillaceous rock in the Goupitan hydroelectric power station, China, are identified. The results show that the algorithm is feasible for rock mechanics use and has a useful ability in finding potential models. The algorithm enables the identification of models and parameters simultaneously and provides a new method for studying the mechanical characteristics of visco-elastic rocks.     

Shortening the search time in optimization of water distribution networks

Various optimization techniques have been implemented for minimizing the costs associated with water distribution networks (WDNs). In this regard, meta-heuristic algorithms have represented the highest efficiency. One of the weaknesses of these algorithms is their high computational costs, which make their implementation sometimes impracticable for optimization of large real life WDNs. In this article an optimization model based on the ant colony optimization algorithm is presented for least cost design of WDNs. In the proposed model, ants select pipe diameters so that the energy loss per unit length of pipes will be in a specific range. In this model, the number of objective function evaluations is small. Two sample networks have been optimized using the proposed model. Obtained results show that the model presented in this article has a very low search time, which makes its implementation possible for large real-life WDNs.     

Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization

The absence of a valid resource-constrained critical path method (CPM) not only hampers the widespread use of mainstream project scheduling software in construction management practice, but also destabilizes the very foundation of any sophisticated, CPM-based time or cost analysis in construction scheduling research. This has motivated us into developing an innovative, fully-automated solution to resource-constrained CPM called the Simplified Simulation-based Scheduling system (short as S3). S3 takes advantage of the simplified discrete event simulation approach (SDESA) and the evolutionary optimization technique called particle swarm optimizer (PSO) to automate the formulation of a resource-constrained schedule with the shortest total project duration. We clarify basic issues of resource scheduling, elaborate on the formation of a CPM simulation model by SDESA, present PSO algorithms, and discuss the PSO solution formulation and simulation–optimization interaction in relation to the development of S3 software. In order to introduce S3 to construction schedulers, we also reference the relevant functionalities and features of Primavera Project Planner (P3) and Microsoft Project, which are applied alongside S3 in two case studies. The first case is a classic textbook example while the second case is based on a real drainage project in Hong Kong. In both cases, S3 eclipses the current CPM software with respect of (1) shortening the total project duration; (2) optimizing provisions of resources of various types; and (3) producing valid total float values to guide schedule implementation.     

基于微粒群算法和模糊神经网络的火灾探测研究

针对火灾探测的特点,将模糊系统和神经网络有机结合,实现模糊系统设计参数的自动调整。采用符合国家标准明火、阴燃火以及厨房环境下的干扰火等作为模糊神经网络的训练样本和测试样本,依据模糊神经网络算法要求,完成了网络结构的设计,并给出相应的计算模型,利用微粒群算法对网络的权值进行学习与训练。结果表明,该算法在探测国家标准火的火灾状态方面具有有效性和可行性。     

混沌模拟退火粒子群优化算法

基于模拟退火思想的粒子群优化算法和混沌粒子群优化算法,提出了混沌模拟退火粒子群优化算法,编写了其具体流程图,并通过两个算例,验证了该算法的效率和有效性,结果表明该方法可行,具有广泛的应用前景。     

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the sear...     

Multi-objective optimization of water distribution networks

Two multi-objective approaches to the consideration of pipe breakage data in water distribution network designs are formulated. Both models are based on the constraint method for multi-objective analysis. One model analyses the relationship between initial capital cost and subsequent repair and maintenance costs. Pipe breakage data is used to restrict the repair costs permitted in the system. The other model examines the relationships between initial pipe costs and the reliability of the pipes within the distribution network. In this second model, both the worst case and average system performance are examined in relation to the cost making model a three-objective approach. The pipe breakage data is used to restrict the expected number of failures allowed in any link. The actual number of expected breaks occurring in each link is then used to develop Poisson-based probabilities of node isolation. Application of the two approaches shows that the information obtained from such multi-objective approaches gives improved understanding into the nature of the issues behind initial cost and repair cost and initial cost and system reliability.     

Ant-colony optimization of pumping schedule to minimize the energy cost using variable-speed pumps in water distribution networks

Despite considerable pumping energy costs in pumping stations of water distribution networks (WDNs), there are not many studies offering pumping schedule in order to optimize pumping energy costs regarding the WDN design. This paper aims to focus on the optimization of the WDN pumping station schedule by means of variable-speed pump (VSP) and using a combination of the ant system iteration best algorithm (AS_ib) and EPANET2.0. In fact, the VSP is implemented to increase the flexibility of pumping station and obtain the pumping schedule with optimized energy cost as the water demand changes during a day. Given that vast search space in optimization process leads to a decrease in the quality of final results, in this study, two heuristic methods coupled with AS_ib are proposed in order to reduce the number of feasible solutions in the search space. The optimization results showed that the proposed heuristic approaches have considerably improved the quality of solutions produced by the AS_ib and enhanced the navigation of the optimization process. The results of optimization for the Richmond network, similar to the genetic algorithm (GA), showed that the AS_ib was capable of improving pumping energy costs. Besides, using the variable-speed pump in an optimized pump scheduling could lead to greater savings (about 10%) in pumping energy costs compared with the single-speed pump (SSP).     

Design optimization of tall steel buildings by a modified particle swarm algorithm

Optimal design of tall buildings, as large‐scale structures, is a rather difficult task. To efficiently achieve this task, the computational performance of the employed standard meta‐heuristic algorithms needs to be improved. One of the most popular meta‐heuristics is particle swarm optimization (PSO) algorithm. The main aim of the present study is to propose a modified PSO (MPSO) algorithm for optimization of tall steel buildings. In order to achieve this purpose, PSO is sequentially utilized in a multi‐stage scheme where in each stage an initial swarm is generated on the basis of the information derived from the results of previous stages. Two large‐scale examples are presented to investigate the efficiency of the proposed MPSO. The numerical results demonstrate the computational advantages of the MPSO algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

边坡临界滑面搜索的改进粒子群优化算法

边坡临界滑面的确定对边坡稳定分析和加固设计极为重要,采用基于变异和二次序列规划的改进粒子群优化算法(VSPSO)进行临界滑面搜索。VSPSO算法中通过变异操作增强粒子群跳出局部最优解的能力,并用二次序列规划(SQP)加速局部搜索,大大提高了粒子群获得全局最优的能力。通过对有解析解的边坡算例进行分析,验证了该算法的准确性及优越性;对澳大利亚计算机应用协会(ACADS)提供的均质边坡、多层土边坡以及含软弱层边坡进行分析,结果表明改进的VSPSO算法搜索所得滑面比传统PSO算法更逼近推荐答案,具有更好的鲁棒性,而且随着边坡复杂程度的增加,更能体现改进VSPSO算法的优越性,具有广阔的应用前景。     

量子粒子群算法在钢框架优化设计中的运用

介绍了标准粒子群算法的基本思想,提出了钢框架抗震优化设计的量子粒子群算法,建立了多层钢框架优化设计数学模型,最后通过一个算例验证了该方法的效率和有效性,结果表明该方法科学可行,具有很好的应用前景。     

基于微粒群优化的智能位移反分析研究

优化技术是影响反分析精度和效率的重要因素,将微粒群优化技术与支持向量机技术结合,提出了一种新的智能位移反分析方法。该方法利用了支持向量表达非线性关系方面的优良特性,可以避免大量的数值计算,同时充分利用微粒群的全局优化、收敛速度快的优点。将提出的方法应用到具体的算例中,比较表明,本方法是一种科学、可行、收敛快、精度高的优秀算法。     

基于粒子群算法的钢结构截面优化设计

介绍了粒子群算法的基本思想、实现步骤并编制了相应的程序,将粒子群算法应用于钢结构截面优化设计问题中,可方便地对各种钢结构截面进行优化设计。最后通过一个算例验证了该方法的效率和有效性,结果表明该方法科学可行,具有很好的应用前景。     

砌体结构基于群集智能的动力检测

本文提出基于新兴的群集智能算法对砌体结构剩余层间刚度进行识别,该方法仅利用环境激励下的结构响应信号,并根据识别的剩余刚度得到砌体结构的强度,从而达到对砌体结构进行安全性评估的目的。模拟结果表明该方法简单高效,易于实现,为已有砌体房屋的安全检测和质量评估提供一种新的方法与手段。