Simulated Annealing–Genetic Algorithm for Transit Network Optimization

This paper presents a mathematical stochastic methodology for transit route network optimization. The goal is to provide an effective computational tool for the optimization of a large-scale transit route network to minimize transfers with reasonable route directness while maximizing service coverage. The methodology includes representation of transit route network solution search spaces, representation of transit route and network constraints, and a stochastic search scheme based on an integrated simulated annealing and genetic algorithm solution search method. The methodology has been implemented as a computer program, tested using previously published results, and applied to a large-scale realistic network optimization problem.     

Reliability-Constrained Optimization of Water Treatment Plant Design Using Genetic Algorithm

A new approach that links genetic algorithm (GA) as an optimization tool with Monte Carlo simulation (MCS)-based reliability program is presented for reliability-constrained optimal design of water treatment plant (WTP). The reliability of a WTP is defined as the probability that it can achieve the desired effluent water quality standard (WQS). The objective function minimizes the treatment cost, subjected to design and performance constraints, and to achieve desired reliability level for meeting the given effluent WQS. The random variables used to generate the reliability estimates are suspended solids (SS) concentration, flow rate, specific gravity of floc particle, temperature of raw water, sedimentation basin performance index, and model coefficients. The application of GA-MCS approach for design of a WTP is illustrated with a hypothetical case study. The annualized cost of WTP is affected by the number of uncertain parameters included in the analysis, coefficient of variation of uncertain parameters, effluent WQS, and target reliability level. Analysis suggests that higher reliability at lower annual cost of treatment can be achieved by limiting the fluctuation of uncertain parameters. Results show that distribution of effluent SS is also affected by the uncertainty. The suggested GA-MCS approach is efficient to evaluate treatment cost-reliability tradeoff for WTP. Results demonstrate that the combination of GA with MCS is an effective approach to obtain the reliability-constrained optimal/near-optimal solution of WTP design problem consistently.     

Intelligent Agent Optimization of Urban Bus Transit System Design

The transit route network design (TRND) problem seeks a set of bus routes and schedules that is optimal in the sense that it maximizes the utility of an urban bus system for passengers while minimizing operator cost. Because of the computational intractability of the problem, finding an optimal solution for most systems is not possible. Instead, a wide variety of heuristic and meta-heuristic approaches have been applied to the problem to attempt to find near-optimal solutions. This paper presents an optimization system that synthesizes aspects of previous approaches into a scalable, flexible, intelligent agent architecture. This architecture has successfully been applied to other transportation and logistics problems in both research studies and commercial applications. This study shows that this intelligent agent system outperforms previous solutions for both a benchmark Swiss bus network system and the very large bus system in Delhi, India. Moreover, the system produces in a single run a set of Pareto equivalent solutions that allow a transit operator to evaluate the trade-offs between operator costs and passenger costs.     

Optimal Design of Composite Channels Using Genetic Algorithm

In the past, studies involving optimal design of composite channels have employed Horton’s equivalent roughness coefficient, which uses a lumped approach in assuming constant velocity across a composite channel cross section. In this paper, a new nonlinear optimization program (NLOP) is proposed based on a distributed approach that is equivalent to Lotter’s observations, which allows spatial variations in velocity across a composite channel cross section. The proposed NLOP, which consists of an objective function of minimizing total construction cost per unit length of a channel, is solved using genetic algorithm (GA). Several scenarios are evaluated, including no restrictions, restricted top width, and restricted channel side slopes, to account for certain site conditions. In addition, the proposed NLOP is modified to include constraints on maximum permissible velocities corresponding to different lining materials of the composite channel cross section, probably for the first time. The proposed methodology is applied to trapezoidal and triangular channel cross sections but can be easily extended to other shapes or compound channels. Optimal design graphs are presented to determine the channel dimensions of a composite trapezoidal channel cross section. The results obtained in this study indicate that cost savings up to 35% can be achieved for the unconstrained velocity case and up to 55% for the limiting velocity case when the proposed NLOP is solved using GA as compared with the existing NLOP solved using either the classical optimization solution technique or GA.     

Water Distribution Network Design Using Heuristics-Based Algorithm

Water distribution network that includes supply reservoirs, overhead tanks, consumer demand nodes, interconnecting pipes, lifting pumps, and control valves is the main mode of water supply for majority of the communities especially in urban areas. Supply of required quantity of water and at right time is the primary objective of water distribution network analysis. The analysis of water distribution networks can be broadly classified into design and operation problems and both problems have been the focus of many researchers over the past three decades. In the water distribution network design problems, the target is attaining the cost effective configuration that satisfies the minimum hydraulic head requirement at the demand nodes. In this paper, a new algorithm for design of water distribution network namely “heuristics-based algorithm” which completely utilizes the implicit information associated with the water distribution network to be designed has been proposed and validated with two water distribution networks. It is found that the proposed algorithm performs well for the least-cost design of water distribution networks.     

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.     

Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects

This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices.     

Optimal Layout Design of a Satellite Module Using a Coevolutionary Method with Heuristic Rules

The layout design of a satellite module belongs to a three-dimensional (3D) packing problem with mutual-conflicting performance constraints. Taking the layout design of a simplified commercial communication satellite as a background, based on the cooperative coevolutionary framework, this paper presents a coevolutionary method with heuristic rules for the optimal layout design of a satellite module. First, a whole satellite module layout problem is decomposed into several sublayout problems according to the multisubphysical structure of a satellite module. Second, a relaxation model is adopted to distribute all objects among subspaces. Third, a coevolutionary genetic algorithm is adopted to solve the detailed layout design within the subspaces. Finally, a heuristic combination-rotation (CR) method is adopted to adjust the constraints to obtain the final whole layout scheme. Compared with the coevolutionary approach and the all-at-once optimization approaches, computational results show that the CR method can improve the computational accuracy of solutions and the proposed heuristic coevolutionary method can produces better solutions within short running times.     

Bridge Model Updating Using Response Surface Method and Genetic Algorithm

A finite-element (FE) model of a structure is a highly idealized engineering model that may or may not truly reflect the physical structure. The purpose of model updating is to modify the FE model of a structure in order to obtain better agreement between the numerical and field-measured structure responses. In this paper, a new practical and user-friendly FE model updating method is presented. The new method utilizes the response surface method for the best experimental design of the parameters to be updated based on which numerical analysis can be performed in order to obtain explicit relationships between the structural responses and parameters from the simulation results. The parameters are then be updated using the genetic algorithm (GA) by minimizing an objective function. A numerical example of a simply supported beam has been used to demonstrate the concept. This method has also been applied to the model updating of an existing bridge. Results show that this method works well and achieves reasonable physical explanations for the updated parameters.     

Use of Genetic Algorithm in Optimization of Irrigation Pumping Stations

Energy costs constitute the largest expenditure for nearly all water utilities worldwide and can consume up to 65% of a water utility’s annual operating budget. One of the greatest potential areas for energy cost savings is in the scheduling of pump operations. This paper presents a new management model, WAPIRRA Scheduler, for the optimal design and operation of water distribution systems. The model makes use of the latest advances in genetic algorithm (GA) optimization to automatically determine annually the least cost of pumping stations while satisfying target hydraulic performance requirements. Optimal design and operation refers to selecting pump type, capacity, and number of units as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a given set of demand curves. The optimization process consists of three main steps: (1) generating randomly an initial set of pump combinations to start the optimization process for a given demand-duration curve; (2) minimizing the total annual cost, which consists of operation and maintenance costs and depreciation cost of the initial investment, by changing the set and discharge of pump sets based on the provided model; and (3) achieving the final criterion to stop the optimization process and reporting the optimized results of the model. Computational analysis is based upon one major objective function and solving it by means of a computer program that is developed following the GA approach to find the optimized solution of generated equations. Application of the model to a real-world project shows considerable savings in cost and energy.     

Provision of Reinforcement in Concrete Solids Using the Generalized Genetic Algorithm

A generalized genetic algorithm has been developed to find the global optimal reinforcement contents for a concrete solid structure subjected to a general three-dimensional (3D) stress field. Feasible solutions were examined based on the genetic algorithm, and the heterogeneous strategy used ensures that all of the local optimal regions are searched and the most optimal reinforcement content found. The effectiveness of the proposed approach has been validated by comparing the steel contents evaluated using the present method with those obtained from other available methods. A more economic design is achieved by the proposed algorithm. The method developed provides the designer with a valuable tool for the determination of reinforcements in complicated solid concrete structures.     

Optimum Design of Welded Steel Plate Girder Bridges Using a Genetic Algorithm with Elitism

The genetic algorithm (GA) is a general optimization technique that has some unique features that are especially suitable for structural engineering problems. This work uses a simple GA with elitism to find the optimum design of welded steel plate girder bridges. The objectives are to minimize the weight and the cost of the girders. Two types of plate-girder bridges are studied: a single-span bridge and a two-equal-span continuous bridge. Bridges with various span lengths, in increments of 20?ft, are investigated; results are tabulated, parametric studies are made, and meaningful conclusions are drawn.     

Comparing Schedule Generation Schemes in Resource-Constrained Project Scheduling Using Elitist Genetic Algorithm

An issue has arisen with regard to which of the schedule generation schemes will perform better for an arbitrary instance of the resource-constrained project scheduling problem (RCPSP), which is one of the most challenging areas in construction engineering and management. No general answer has been given to this issue due to the different mechanisms between the serial scheme and the parallel scheme. In an effort to address this issue, this paper compares the two schemes using a permutation-based Elitist genetic algorithm for the RCPSP. Computational experiments are presented with multiple standard problems. From the results of a paired difference experiment, the algorithm using the serial scheme provides better solutions than the one using the parallel scheme. The results also show that the algorithm with the parallel scheme takes longer to solve each problem than the one using the serial scheme.     

Efficient Algorithm for Computing Einstein Integrals

Analytical approximations to Einstein integrals are proposed. The approximations represented by two fast-converging series are valid for all values of their arguments. Accordingly, the algorithm can be easily incorporated into professional software like HEC-RAS or HEC-6 with minimum computational effort.     

Collision Free Path Planning of Cooperative Crane Manipulators Using Genetic Algorithm

This paper presents a new approach for automated path planning of cooperative crane manipulators using a genetic algorithm (GA). The inverse kinematic problem, i.e., determining the joint angle configuration for the cooperative crane manipulator system in moving the object from pick location to place location, is defined as an optimization problem and solved using GA. For generating the collision-free path, GA with an interference detection algorithm is employed and search is made in the manipulator joint angle space (configuration space). The effectiveness of the proposed approach for automated path planning is demonstrated by comparing the performance of the present approach with the earlier heuristic search proposed by Sivakumar et al. The GA approach finds a near-optimal path with lower path cost and less computational time than earlier heuristic searches.     

Design of a Single-Pool Downstream Controller Using Quantitative Feedback Control Theory

A downstream controller is designed for an irrigation canal reach using a design technique called quantitative feedback control theory (QFT). The performance of this controller is compared to a proportional, integral, derivative (PID) controller and a linear quadratic regulator (LQR) controller. In this study, the QFT controller is designed for a single canal reach because it best demonstrates how a controller is designed. Previous research for this canal model provided data for comparison. For the operating conditions that are defined in this paper, the QFT controller is shown to have slightly better performance than the PID controller and better performance than the LQR controller. When the canal hydraulic roughness is increased, the QFT controller still performed better than the PID controller.     

Unstructured Grid Finite-Volume Algorithm for Shallow-Water Flow and Scalar Transport with Wetting and Drying

A high-resolution, unstructured grid, finite-volume algorithm is developed for unsteady, two-dimensional, shallow-water flow and scalar transport over arbitrary topography with wetting and drying. The algorithm uses a grid of triangular cells to facilitate grid generation and localized refinement when modeling natural waterways. The algorithm uses Roe’s approximate Riemann solver to compute fluxes, a multidimensional limiter for second-order spatial accuracy, and predictor–corrector time stepping for second-order temporal accuracy. The novel aspect of the algorithm is a robust and efficient procedure to consistently track fluid volume and the free surface elevation in partially submerged cells. This leads to perfect conservation of both fluid and dissolved mass, preservation of stationarity, and near elimination of artificial concentration and dilution of scalars at stationary or moving wet/dry interfaces. Multi-dimensional slope limiters, variable reconstruction, and flux evaluation schemes are optimized in the algorithm on the basis of accuracy per computational effort.     

Vibration-based Damage Detection of Structures by Genetic Algorithm

Vibration-based methods are being rapidly applied to detect structural damage. The usual approaches incorporate sensitivity analysis and the optimization algorithm to minimize the discrepancies between the measured vibration data and the analytical data. However, conventional optimization methods are gradient based and usually lead to a local minimum only. Genetic algorithms explore the region of the whole solution space and can obtain the global optimum. In this paper, a genetic algorithm with real number encoding is applied to identify the structural damage by minimizing the objective function, which directly compares the changes in the measurements before and after damage. Three different criteria are considered, namely, the frequency changes, the mode shape changes, and a combination of the two. A laboratory tested cantilever beam and a frame are used to demonstrate the proposed technique. Numerical results show that the damaged elements can be detected by genetic algorithm, even when the analytical model is not accurate.     

Domain-by-Domain Algorithm for Nonlinear Finite-Element Analysis of Structures

Parallel and distributed computers have been shown to provide the necessary computational power to solve large-scale engineering problems. However, in order for this computation style to be effectively used, efficient computational algorithms must be devised. In this work, a domain-by-domain algorithm is developed for the parallel solution of nonlinear structural dynamics problems. In the proposed algorithm, the original structure is partitioned into a number of subdomains. Each subdomain is solved independently and, therefore, concurrently using a traditional direct-solution method. Finally, the solution for the interface degrees of freedom between neighboring subdomains is obtained by enforcing compatibility and equilibrium using an iterative procedure. The nonlinear version of the algorithm involves two iterations: The nonlinear dynamic equilibrium iteration and the interface equilibrium and compatibility iteration. The integration of these two iterations is investigated and two strategies are developed. It is found that the strategy in which the two iterations are isolated is the most efficient. As a demonstration, the fully nonlinear transient analysis of a 20-story model building is carried out. Excellent accuracy in the solution and significant speed up values are obtained.     

Evaluation of a Genetic Algorithm for the Irrigation Scheduling Problem

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets which may be serviced simultaneously. This problem has an analogy with the classical earliness/tardiness problem in operations research. In previously published work an integer program was used to solve this problem, however such scheduling problems belong to a class of combinatorial problems known to be computationally demanding (N-P hard). This is widely reported in operations research. Hence integer programs can only be used to solve relatively small problems usually 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 as reported in the literature, these need to be formulated carefully and tested thoroughly. This paper demonstrates the importance of robust testing of one such genetic algorithm formulated to solve the irrigation scheduling problem with simultaneous outlets serviced against an integer program formulated to solve the same problem.