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.     

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.     

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.     

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.     

Multireservoir Systems Optimization Using Genetic Algorithms: Case Study

A genetic algorithm approach is presented for the optimization of multireservoir systems. The approach is demonstrated through application to a reservoir system in Indonesia by considering the existing development situation in the basin and two future water resource development scenarios. A generic genetic algorithm model for the optimization of reservoir systems has been developed that is easily transportable to any reservoir system. This generality is a distinct practical advantage of the genetic algorithm approach. A comparison of the genetic algorithm results with those produced by discrete differential dynamic programming is also presented. For each case considered in this study, the genetic algorithm results are very close to the optimum, and the technique appears to be robust. Contrary to methods based on dynamic programming, discretization of state variables is not required. Further, there is no requirement for trial state trajectories to initiate the search using a genetic algorithm. Model sensitivity and generalizations that can be drawn from this and earlier work by Wardlaw and Sharif are also considered.     

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.     

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.     

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.     

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.     

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.     

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.     

Microwave Subsurface Imaging Technology for Damage Detection

This paper presents a technology for detecting invisible damage inside concrete, which is based on reconstruction of dielectric profile (image) of the concrete illuminated with microwaves sent from and received by antenna arrays controlled by specialized software. The imaging system developed in this study consists of an 8×8 transmitting and an 8×8 receiving arrays, an innovative numerical bifocusing operator for improving image resolution, and imaging software for reconstructing a two-dimensional image from the scattered field. The effectiveness of the developed technology in detecting steel and voids inside concrete has been demonstrated through numerical simulation and experiments.     

Irrigation Scheduling with Genetic Algorithms

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets that may be serviced simultaneously. This problem has an analogy with the classical multimachine earliness/tardiness scheduling problem in operations research (OR). In previously published work, integer programming was used to solve irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding. This is widely reported in OR literature. Hence integer programs (IPs) can be used only to solve relatively small problems typically in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. However, these need to be formulated carefully and tested thoroughly. The current research explores the potential of genetic algorithms to solve the simultaneous irrigation scheduling problem. For this purpose, two models are presented: the stream tube model and the time block model. These are formulated as genetic algorithms, which are then tested extensively, and the solution quality is compared with solutions from an IP. The suitability of these models for the simultaneous irrigation scheduling problem is reported.     

Subsurface Soil-Geology Interpolation Using Fuzzy Neural Network

Soil geology plays an important role in selection of core soil for constructing rock-fill dams and in geotechnical evaluation while constructing major structures. Inferring the geology formations in the region between one borehole and another (cross-borehole region) is a human-intensive process of only moderate reliability. Improved operation planning and better geological assessment contributing to cost reduction can be achieved if reliability of inference can be improved. Cross-borehole interpolation using neural networks, such as the multilayer perceptron (MLP), is a relatively recent development and offers many advantages in dealing with the nonlinearity inherent in such a problem. However, neural networks alone are not sufficient to accommodate the fuzzy nature of the geological information. Cross-borehole soil-geology interpolation was investigated using a fuzzy-MLP neural network and is summarized in this paper. To train this network, data from borehole investigations were supplemented with artificial data created using human knowledge, which we term “data-based knowledge incorporation.” The fuzzy-MLP neural network takes advantage of MLP neural networks and fuzzy set theory. Because of this, fuzzy-MLP not only interpolates but also provides an indication about the interpolation accuracy.     

Genetic Algorithms in Competitive Environments

Competition is introduced among the populations of a number of genetic algorithms (GAs) in solving optimization problems. The aim is to adapt the parameters of the GAs, by altering the resources of the system, so as to achieve better solutions. The evolution of the different populations, having different sets of parameters, is controlled at the level of metapopulation, i.e., the union of populations, on the basis of statistics and trends of the evolution of every population. An overall fitness measure is introduced that incorporates a diversity measure and the required resources to rank the populations. The fuzzy outcome of the conflict among the populations guides the evolution of the different GAs toward better solutions in the statistical sense. The proposed scheme is applied to two different problems—a multimodal function with six global and several near-global optima, and a reliability based optimal design of a simple truss. Numerical results are presented, and the robustness and computational efficiency of the proposed scheme are discussed.     

Comparison of Using Mixed-Integer Programming and Genetic Algorithms for Construction Site Facility Layout Planning

The use of modular construction has gained wide acceptance in the industry. For a specific construction facility layout problem such as site precast standardized modular units, it requires the establishment of an on-site precast yard. Arranging the precast facilities within a construction site presents real challenge to site management. This complex task is further augmented with the involvement of several resources and different transport costs. A genetic algorithm (GA) model was developed for the search of a near-optimal layout solution. Another approach using mixed-integer programming (MIP) has been developed to generate optimal facility layout. These two approaches are applied to solve with an example in this paper to demonstrate that the solution quality of MIP outperforms that of GA. Further, another scenario with additional location constraints can also be solved readily by MIP, which, however, if modeled by GA, the solution process would be complicated. The study has highlighted that MIP can perform better than GA in site facility layout problems in which the site facilities and locations can be represented by a set of integer variables.     

Minimizing Cutting Wastes of Reinforcement Steel Bars Using Genetic Algorithms and Integer Programming Models

Materials that are in the form of one-dimensional stocks such as steel rebars, structural steel sections, and dimensional lumber generate a major fraction of the generated construction waste. Cutting one-dimensional stocks to suit the construction project requirements result in trim or cutting losses, which is the major cause of the one-dimensional construction waste. The optimization problem of minimizing the trim losses is known as the cutting stock problem (CSP). In this paper, three approaches for solving the one-dimensional cutting stock problem are presented. A genetic algorithm (GA) model, a linear programming (LP) model, and an integer programming (IP) model were developed to solve the one-dimensional CSP. Three real life case studies from a steel workshop have been studied. The generated cutting schedules using the GA, LP, and IP approaches are presented and compared to the actual workshop’s cutting schedules. The comparison shows a high potential of savings that could be achieved using such techniques. Additionally, a user friendly Visual Basic computer program that utilizes genetic algorithms for solving the one-dimensional CSP is presented.     

Subsurface Characterization Using Artificial Neural Network and GIS

A method for characterizing the subsurface is developed using an artificial neural network (ANN) and geographic information system (GIS). Data on the distribution of aquifer materials from monitoring well lithologic logs are used to train a multilayer perceptron using the back-propagation algorithm. The trained ANN predicts using an appropriate prediction scale, the subsurface formation materials at each point on a discretized grid of the model area. GIS is then used to develop subsurface profiles from the data generated using the ANN. These subsurface profiles are then compared with available geological sections to check the accuracy of the ANN-GIS generated profiles. This methodology is applied to determine the aquifer extent and calculate aquifer parameters for input to ground-water models for the multiaquifer system underlying the city of Bangkok, Thailand. A selected portion of the model domain is used for illustration. The integrated approach of ANN and GIS is shown to be a powerful tool for characterizing complex aquifer geometry, and for calculating aquifer parameters for ground-water flow modeling.     

Interactive Analysis of Spatial Subsurface Data Using GIS-Based Tool

Much of the spatial information that is an inherent part of geotechnical and geoenvironmental investigation programs has been underutilized due to the unavailability of analysis tools designed to take advantage of the spatial attributes of the information. This paper describes a system developed to provide a quantitative estimate of the adequacy of coordinate-based information used to make particular decisions, with particular emphasis on geotechnical and geoenvironmental site investigation information. The system was developed using a geographic information system (GIS) as a platform that was integrated with an existing commercial geostatistical software package to allow for more comprehensive analysis and optimization capabilities.