Optimization of Pile Groups Using Hybrid Genetic Algorithms

This paper presents an automated optimal design method using a hybrid genetic algorithm for pile group foundation design. The design process is a sizing and topology optimization for pile foundations. The objective is to minimize the material volume of the foundation taking the configuration, number, and cross-sectional dimensions of the piles as well as the thickness of the pile cap as design variables. A local search operator by the fully stressed design (FSD) approach is incorporated into a genetic algorithm (GA) to tackle two major shortcomings of a GA, namely, large computation effort in searching the optimum design and poor local search capability. The effectiveness and capability of the proposed algorithm are first illustrated by a five by five pile group subjected to different loading conditions. The proposed optimization algorithm is then applied to a large-scale foundation project to demonstrate the practicality of the algorithm. The proposed hybrid genetic algorithm successfully minimizes the volume of material consumption and the result matches the engineering expectation. The FSD operator has great improvement on both design quality and convergence rate. Challenges encountered in the application of optimization techniques to design of pile groups consisting of hundreds of piles are discussed.     

Resource Optimization Using Combined Simulation and Genetic Algorithms

This paper presents a new approach for resource optimization by combining a flow-chart based simulation tool with a powerful genetic optimization procedure. The proposed approach determines the least costly, and most productive, amount of resources that achieve the highest benefit/cost ratio in individual construction operations. To further incorporate resource optimization into construction planning, various genetic algorithms (GA)-optimized simulation models are integrated with commonly used project management software. Accordingly, these models are activated from within the scheduling software to optimize the plan. The result is a hierarchical work-breakdown-structure tied to GA-optimized simulation models. Various optimization experiments with a prototype system on two case studies revealed its ability to optimize resources within the real-life constraints set in the simulation models. The prototype is easy to use and can be used on large size projects. Based on this research, computer simulation and genetic algorithms can be an effective combination with great potential for improving productivity and saving construction time and cost.     

Object-Oriented Optimization Approach Using Genetic Algorithms for Lattice Towers

A new approach is presented for the optimization of steel lattice towers by combining genetic algorithms and an object-oriented approach. The purpose of this approach is to eliminate the difficulties in the handling of large size problems such as lattice towers. Improved search and rapid convergence are obtained by considering the lattice tower as a set of small objects and combining these objects into a system. This is possible with serial cantilever structures such as lattice towers. A tower consists of panel objects, which can be classified as separate objects, as they possess an independent property as well as inherent properties. This can considerably reduce the design space of the problem and enhance the result. An optimization approach for the steel lattice tower problem using objects and genetic algorithms is presented here. The paper also describes the algorithm with practical design considerations used for this approach. To demonstrate the approach, a typical tower configuration with practical constraints has been considered for discrete optimization with the new approach and compared with the results of a normal approach in which the full tower is considered.     

Optimization of Resource Allocation and Leveling Using Genetic Algorithms

Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA procedure. A case study is presented and several experiments conducted to demonstrate the multiobjective benefit of the procedure and outline future extensions.     

Trihalomethane Species Forecast Using Optimization Methods: Genetic Algorithms and Simulated Annealing

Chlorination is an effective method for disinfection of drinking water. Yet chlorine is a strong oxidizing agent and easily reacts with both organic and inorganic materials. Trihalomethanes (THMs), formed as a by-product of chlorination, are carcinogenic to humans. Models can be derived from linear and nonlinear multiregression analyses to predict the THM species concentration of empirical reaction kinetic equations. The main objective of this study is to predict the concentrations of THM species by minimizing the nonlinear function, representing the errors between the measured and calculated THM concentrations, using the genetic algorithm (GA) and simulated annealing (SA). Additionally, two modifications of SA are employed. The solutions obtained from GA and SA are compared with the measured values and those obtained from a generalized reduced gradient method (GRG2). The results indicate that the proposed heuristic methods are capable of optimizing the nonlinear problem. The predicted concentrations may provide useful information for controlling the chlorination dosage necessary to assure the safety of water drinking.     

Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

A data base was put together for the mechanical properties of microalloyed steels, which contained about 800 entries for ultimate tensile strength (UTS), yield strength (YS), and elongation. Using an evolutionary neural network, based upon a predator–prey genetic algorithms of bi‐objective type, this information was used to construct data‐driven models for UTS, YS, and elongation. The optimum Pareto tradeoffs between these properties were obtained using a multi‐objective genetic algorithm. The results led to some hitherto unexplored steel compositions with optimum properties. Some such steels were actually cast and the experimentally observed property values were found to be well in accord with the predicted results.     

Concurrent Genetic Algorithms for Optimization of Large Structures

In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high‐rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high‐performance computers such as the Cray Y‐MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high‐rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35‐story tower (with 1,262 elements and 936 degrees of freedom), using eight processors.     

Effects of Local Search Algorithms on Groundwater Remediation Optimization Using a Self-Adaptive Hybrid Genetic Algorithm

Genetic algorithms allow solution of more complex, nonlinear civil, and environmental engineering problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve algorithm performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). The inclusion of local search helps to speed up the solution process and to make the solution technique more robust. This paper focuses on the effects of different local search algorithms on the performance of two different HGAs developed in previous phases of this research, the self-adaptive hybrid genetic algorithm (SAHGA) and the enhanced SAHGA. The algorithms are tested on eight test functions from the genetic and evolutionary computation literature and a groundwater remediation design case study. The results show that the selection of the local search algorithm to be combined with the simple genetic algorithm is critical to algorithm performance. The best local search algorithm varies for different problems, but can be selected prior to solving the problem by examining the reduction in fitness standard deviation associated with each local search algorithm, and the time distribution associated to the local search algorithm.     

Achieving Water Quality System Reliability Using Genetic Algorithms

This paper presents an efficient approach for obtaining wasteload allocation solutions that provide the optimal trade-off between treatment cost and reliability. This approach links a genetic algorithm (GA) with the first-order reliability method (FORM) for estimating the probability of system failure under a given wasteload allocation. The GA-FORM optimization approach is demonstrated for the case study of managing water quality in the Willamette River in Oregon. The objective function minimizes the sum of the treatment cost and the penalty associated with breaching a reliability target for meeting a water quality standard. The random variables used to generate the reliability estimates include streamflow, temperature, and reaeration coefficient values. The results obtained indicate that the GA-FORM approach is nearly as accurate as the approach that links the GA with Monte Carlo simulation and is far more efficient. The trade-off between total treatment cost and reliability becomes more pronounced at higher water quality standards and is most sensitive to the uncertainty in the reaeration coefficient. The sensitivity to the reaeration coefficient also increases at increased reliability levels.     

Subsurface Biological Activity Zone Detection Using Genetic Search Algorithms

Use of generic search algorithms for detection of subsurface biological activity zones (BAZ) is investigated through a series of hypothetical numerical biostimulation experiments. Continuous injection of dissolved oxygen and methane with periodically varying concentration stimulates the cometabolism of indigenous methanotropic bacteria. The observed breakthroughs of methane are used to deduce possible BAZ in the subsurface. The numerical experiments are implemented in a parallel computing environment to make possible the large number of simultaneous transport simulations required by the algorithm. Our results show that genetic algorithms are very efficient in locating multiple activity zones, provided the observed signals adequately sample the BAZ.     

Finance-Based Scheduling: Tool to Maximize Project Profit Using Improved Genetic Algorithms

Contractor’s ability to procure cash to carry out construction operations represents a crucial factor to run profitable business. Bank overdrafts have always been the major source to finance construction projects. However, it is not uncommon that bankers set a limit on the credit allocated to an established overdraft. Bankers’ interest rates and consequently contractors’ financing costs are basically determined based on the allocated credit limits. Furthermore, project indirect costs are directly proportional to the project duration which is affected by the allocated credit limit. Thus, the credit limit affects project financing costs and indirect costs which in turn affect project profit. However, finance-based scheduling produces financially executable schedules at specified credit limits while maintaining the demand of time minimization. Thus, finance-based scheduling provides a tool to control the credit requirements. This control enables contractors to negotiate lower interest rates which reduce financing costs. Thus, finance-based scheduling enables contractors to reduce project indirect costs and financing costs. This paper utilizes genetic algorithm’s technique to devise finance-based schedules that maximize project profit through minimizing financing costs and indirect costs.     

基于遗传算法的工作辊温度场参数优化模型

针对热连轧机工作辊热辊形计算中温度场模型的热交换等参数难以确定的问题 ,建立了基于遗传算法的参数优化模型 ,可以解决复杂条件下的热参数的求解问题。利用优化参数计算的轧辊温度场与实际测量结果一致。     

Preliminary Cost Estimation Model Using Case-Based Reasoning and Genetic Algorithms

This study proposes a preliminary cost estimation model using case-based reasoning (CBR) and genetic algorithm (GA). In measuring similarity and retrieving similar cases from a case base for minimum prediction error, it is a key process in determining the factors with the greatest weight among the attributes of cases in the case base. Previous approaches using experience, gradient search, fuzzy numbers, and analytic hierarchy process are limited in their provision of optimal solutions. This study therefore investigates a GA for weight generation and applies it to real project data. When compared to a conventional construction cost estimation model, the accuracy of the CBR- and GA-based construction cost estimation model was verified. It is expected that a more reliable construction cost estimation model could be designed in the early stages by using a weight estimation technique in the development of a construction cost estimation model.     

Object-Oriented Framework for Genetic Algorithms with Application to Space Truss Optimization

Genetic algorithms have been shown to be very effective optimization tools for a number of engineering problems. Since the genetic processes typically operate independent of the actual problem, a core genetic algorithm library consisting of all the genetic operators having an interface to a generic objective function can serve as a very useful tool for learning as well as for solving a number of practical optimization problems. This paper discusses the object-oriented design and implementation of such a core library. Object-oriented design, apart from giving a more natural representation of information, also facilitates better memory management and code reusability. Next, it is shown how classes derived from the implemented libraries can be used for the practical size optimization of large space trusses, where several constructibility aspects have been incorporated to simulate real-world design constraints. Strategies are discussed to model the chromosome and to code genetic operators to handle such constraints. Strategies are also suggested for member grouping for reducing the problem size and implementing move-limit concepts for reducing the search space adaptively in a phased manner. The implemented libraries are tested on a number of large previously fabricated space trusses, and the results are compared with previously reported values. It is concluded that genetic algorithms implemented using efficient and flexible data structures can serve as a very useful tool in engineering design and optimization.     

免疫遗传算法对精轧机组负荷分配的优化

提出基于IGA模型的热连轧精轧机组负荷分配的智能优化新方法,该方法具有计算精度高、速度快等特点,且适合在线计算.实验数据对比分析结果表明了该方法的有效性,为热连轧精轧机组轧制规程的智能优化设计提供了一条新途径.     

Optimal Reservoir Operation for Irrigation of Multiple Crops Using Genetic Algorithms

This paper presents a genetic algorithm (GA) model for obtaining an optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area. The model takes into account reservoir inflow, rainfall on the irrigated area, intraseasonal competition for water among multiple crops, the soil moisture dynamics in each cropped area, the heterogeneous nature of soils, and crop response to the level of irrigation applied. The model is applied to the Malaprabha single-purpose irrigation reservoir in Karnataka State, India. The optimal operating policy obtained using the GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.     

Web-Based Decision Support Systems: Case Study inProject Delivery

The decentralized nature of the construction industry contributes to difficulties in the implementation and dissemination of project management-based decision tools. The majority of decision support systems (DSS) are contained in-house with private developers and users, or on researchers stand-alone computers and academic journals. Current World Wide Web technologies provide the appropriate means for large-scale implementation and continued development of DSS for the architectural, engineering, and construction community. This paper documents a DSS developed specifically for design∕build project selection among United States public sector agencies. The system, the Design∕Build Selector (DBS), is Web enabled, allowing for easy access and large-scale dissemination. Design∕build project procurement is rapidly expanding throughout public sector agencies in the United States construction industry. As public agencies turn to design∕build, appropriate project selection is a primary consideration affecting successful delivery. Prior to the implementation of DBS, there was no systematic or formalized method for selecting projects appropriate for design∕build. Since 1997, the Web site that houses the DBS has been visited by over 4,000 people, and the DBS tool has been used on 102 projects representing over $4.8 billion in construction. This paper reports on the application and potential for Web-based DSS in civil engineering. The architecture of the program, data collection, model weighting, and output interface are explained.     

一种改进的遗传算法用于PI控制器的参数寻优

以武钢1700mm精轧机主传动系统中控制对象特性参数的改变后系统动态响应品质变差为背景,利用遗传送代算法研究了双闭环控制系统中转速PI调节器参数的优化问题.数字仿真结果证明了该算法的有效性和合理性.     

基于遗传算法轴对称梯度功能材料优化设计

基于计算数学理论与热弹性原理,针对环状截面梯度功能材料(FGM)提出了热应力分布公式.以沿坐标轴的最大热应力达到最小为目标,建立了轴对称FGM多目标优化模型.应用遗传算法实现了优化设计,获得了稳态温度场下环状截面FGM的最佳材料分布参数.