Search

To accelerate the convergence rate of high-dimensional optimization problems, inspired by the cooperative hunting process of spider colonies named Anelosimus eximius, a new A. eximius colony algorithm (AECA) was proposed to solve combinatorial optimization problems. In the AECA, a certain direction component of the problem solution is represented as a certain direction in which a spider travels, so that a high-dimensional optimization problem can be transformed into multiple low-dimensional optimization problems. The AECA includes two intelligent behaviors: the random walk of spiders and the summoning of the initiator. The random walk of spiders ensures the diversity of spider colonies, whereas the summoning of the initiator can accelerate the convergence rate. We theoretically proved that the AECA is globally convergent. The inversion method of asteroid spectrum reflectance template can be used to solve the problem that the measured planetary spectrum is affected by the asteroid absorption effect and improves the accuracy of celestial Doppler difference velocimetry, which uses celestial spectrum to provide the information of velocity measurement for navigation. The essence of this method is the optimal combination problem of intrinsic mode functions (IMFs). We applied the AECA to the inversion of the planetary spectrum reflectance template. Experimental results show that, compared with genetic algorithms (GAs), the AECA can obtain the optimal combination of spectrum reflectance templates faster. In addition, to verify the universality of the AECA, for the classical knapsack problem, the AECA has a better optimization effect, faster convergence rate, and higher stability than other swarm intelligence algorithms such as GA, discrete particle swarm optimization, and the quantum genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of particle swarm optimization (PSO) to the programming of pavement maintenance activities at the network level. Furthermore, the application of the PSO technique and its relevance to solving the programming problem in a pavement management system (PMS) is discussed. The robustness and quick search capability of PSO enables it to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of PSO in accommodating different forms of objective functions and comparing the results with the genetic algorithm (GA). This paper compares PSO and GA with respect to rate of convergence and accuracy of modeling PMS using an example problem. The results of this paper confirmed the potential of PSO to successfully model the PMS. [ABSTRACT FROM AUTHOR]

A noncrossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed in this paper. This CMBGA differs from the classic genetic algorithm (GA) optimization in that it does not utilize the crossover operator; instead, it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run, rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about (where = number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately previously has been demonstrated to be an effective value and is commonly used for the GA. Two case studies are used to investigate the effectiveness of the proposed CMBGA. An objective of this paper is to compare the performance of the proposed CMBGA with four other GA variants and other published results. The results show that the proposed CMBGA exhibits considerable improvement over the considered GA variants, and comparable performance with respect to other previously published results. Two big advantages of the CMBGA are its simplicity and the fact that it requires the tuning of fewer parameters compared with other GA variants. [ABSTRACT FROM AUTHOR]

This paper reports the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multiobjective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives, as follows: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age, and (6) fire-fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This paper demonstrates that a many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision-making in the WDS design process. [ABSTRACT FROM AUTHOR]

Existing methods for estimating origin–destination (O–D) matrix from link traffic counts cannot consider multiple-vehicle information, which is easily obtained from a field survey. Those methods use a single-vehicle information and it may decrease the performance of an O–D matrix estimator. Up until now, there is little research regarding multiple-vehicle O–D matrix estimation from multiple-vehicle link counts. This paper suggests a multiple-vehicle O–D matrix estimation from traffic counts using a genetic algorithm (GAMUC). From the numerical tests, we found that the performance of GAMUC was better than those of the previous methods for the error of target O–D matrix and for the number of link counts. In this paper, we also found that the method provided good results on the error of target O–D matrix and on the varied O–D matrix structure, which occurred when the target O–D matrix was different from the true one. [ABSTRACT FROM AUTHOR]

This paper presents a coupled self-adaptive multiobjective differential evolution and network flow algorithm for the optimal operation of complex multipurpose reservoir systems. The developed algorithm (i.e., self-adaptive multiobjective differential evolution) is compared to nondominated sorting genetic algorithm II using a set of common test problems and a real-world case study. An out-of- kilter method for minimal-cost flow problems is used to optimize the water resource system from self-adaptive multiobjective differential evolution inputs driven by the evolutionary process. Self-adaptive multiobjective differential evolution is then used to evaluate objective functions based on the outputs from out-of-kilter algorithm and the process continues until the stop criterion is met. The advantages of the proposed approach include (1) flexible evolutionary algorithms for solving highly complex objective function, and (2) efficient network flow method for dealing with large and highly constrained problems. The case study includes one part of a complex water supply system located in southwestern Brazil that provides water for almost 20 million people in Sao Paulo metropolitan area. The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. The coupled model is applied to the case study using one inflow scenario representing a drought period with inflows below historical average. Multiobjective analyses are performed by comparing two pairs of objective functions, as follows: (1) minimization of demand shortage versus minimization of pumping cost, and (2) minimization of demand shortage versus minimization of the deviation from the water quality standards. The problem constraints include reservoir capacity, capacity of tunnels, channel flow limitations, and minimum downstream release for all reservoirs within the system. The proposed coupled model (self-adaptive multiobjective differential evolution and out-of-kilter) is outperforming both pure self-adaptive multiobjective differential evolution and nondominated sorting genetic algorithm II, as it requires significantly smaller number of generations to derive the Pareto front. In addition, the proposed approach is capable of handling larger problems without major computational burden. The coupled model and self-adaptive multiobjective differential evolution also converge closer to, and provide better coverage of the true Pareto front than, nondominated sorting genetic algorithm II. [ABSTRACT FROM AUTHOR]

This paper presents a case study on the application of a dynamic framework for the intelligent control of flooding in the Boise River system in Idaho. This framework couples a robust and numerically efficient hydraulic routing approach with the popular multi-objective nondominated sorting genetic algorithm II (NSGA-II). The novelty of this framework is that it allows for controlled flooding when the conveyance capacity of the river system is exceeded or is about to exceed. Controlled flooding is based on weight factors assigned to each reach of the system, depending on the amount of damage that would occur, should a flood occur. For example, an urban setting would receive a higher weight factor than a rural or agricultural area. The weight factor for a reach does not need to be constant as it can be made a function of the flooding volume (or water stage) in the reach. The optimization algorithm minimizes flood damage by favoring low-weighted floodplain areas (e.g., rural areas) rather than high-weighted areas (e.g., urban areas) for the overbank flows. The proposed framework has the potential to improve water management and use of flood-prone areas in river systems, especially of those systems subjected to frequent flooding. This work is part of a long-term project that aims to develop a reservoir operation model that combines short-term objectives (e.g., flooding) and long-term objectives (e.g., hydropower, irrigation, water supply). The scope of this first paper is limited to the application of the proposed framework to flood control. Results for the Boise River system show a promising outcome in the application of this framework for flood control. [ABSTRACT FROM AUTHOR]

This paper examines a linked simulation-optimization procedure based on the combined application of an artificial neural network (ANN) and genetic algorithm (GA) with the aim of developing an efficient model for the multiobjective management of groundwater lenses in small islands. The simulation-optimization methodology is applied to a real aquifer in Kish Island of the Persian Gulf to determine the optimal groundwater-extraction while protecting the freshwater lens from seawater intrusion. The initial simulations are based on the application of SUTRA, a variable-density groundwater numerical model. The numerical model parameters are calibrated through automated parameter estimation. To make the optimization process computationally feasible, the numerical model is subsequently replaced by a trained ANN model as an approximate simulator. Even with a moderate number of input data sets based on the numerical simulations, the ANN metamodel can be efficiently trained. The ANN model is subsequently linked with GA to identify the nondominated or Pareto-optimal solutions. To provide flexibility in the implementation of the management plan, the model is built upon optimizing extraction from a number of zones instead of point-well locations. Two issues are of particular interest to the research reported in this paper are: (1) how the general idea of minimizing seawater intrusion can be effectively represented by objective functions within the framework of the simulation-optimization paradigm, and (2) the implications of applying the methodology to a real-world small-island groundwater lens. Four different models have been compared within the framework of multiobjective optimization, including (1) minimization of maximum salinity at observation wells, (2) minimization of the root mean square (RMS) change in concentrations over the planning period, (3) minimization of the arithmetic mean, and (4) minimization of the trimmed arithmetic mean of concentration in the observation wells. The latter model can provide a more effective framework to incorporate the general objective of minimizing seawater intrusion. This paper shows that integration of the latest innovative tools can provide the ability to solve complex real-world optimization problems in an effective way. [ABSTRACT FROM AUTHOR]

Optimal design of water distribution networks (WDN) involves an evaluation of both consumers' pressure benefits and investors' economic objectives. The aforementioned objectives often conflict, so finding the optimal solution for one of those objectives reduces the other objective's utility. In such situations, there are many nondominated solutions, each solution denoting an alternative that cannot be preferred over another in terms of both objectives. Thus, an appropriate alternative to fulfill both objectives and satisfy decision makers' criteria and meet the design purposes within a desirable range necessitates the use of bargaining models that are called conflict-resolution models. This paper considers two urban WDN optimization design problems having different objectives, including initial costs and hydraulic performance improvement of the network by satisfying given hydraulic constraints. First, a set of alternatives are drawn out by a fast messy genetic algorithm (FMGA), then the appropriate alternative design is achieved by using Nash's and Young's bargaining models. Thus, the methodology presented in this paper can be employed by designers of WDNs to simultaneously consider the utilities of both consumers and investors that are the main beneficiaries of such infrastructures. Results show that in almost all the alternatives obtained by the proposed methodology, at least 80% of the most probable utility of both beneficiaries is obtained at the same time, which indirectly indicates the low vulnerability of the design alternative by the considered methodology in meeting the goals of each beneficiary. Moreover, results also indicate that when using the same utility functions, decision points obtained from the two models coincide. [ABSTRACT FROM AUTHOR]

Multicropping is the practice of growing two or more crops in the same space during a single growing season. Planning rules are mathematical equations that use previous experiences of a water resource system to balance the system's water supply and demand, and calculate multicrop areas in various periods. In this paper, linear and nonlinear planning rules are developed for optimal multicrop irrigation areas associated with reservoir operation policies in a reservoir-irrigation system. Reservoir operations are related to water allocations to each irrigated area by considering inflow and storage volume of the reservoir as the water supply in a monthly operation period. Evolutionary algorithms (EAs) can determine optimal multicropping patterns planning rules by considering various mathematical patterns. In this paper, three EAs, namely, (1) genetic algorithm (GA), (2) particle swarm optimization (PSO), and (3) shuffled frog leaping algorithm (SFLA) are employed and compared to maximize the total net benefit of the water resource system by supplying irrigation water for a proposed multicropping pattern over the planning horizon. Results show that the SFLA achieves the best solution, with the maximum value of the objective function in both linear and nonlinear planning rules compared to the GA and PSO. Moreover, the best yield of nonlinear rules is 45.52% better (higher) than that obtained by linear rules. [ABSTRACT FROM AUTHOR]

Streams and their associated biological communities are among our most valuable natural resources. Humans rely on the environmental services provided by streams in a myriad of ways. However, in some areas, excessive groundwater pumping exacerbates the already critical pressure on streamflow and must be managed through effective planning. Based on economic and hydrogeological concepts, this study estimates the quantity of streamflow depletion that is attributable to groundwater pumping and the negative impact on the socioeconomic system if groundwater pumping must be constrained to restore streamflow. The primary objective of this paper is to develop a multiobjective nonlinear optimization model to simulate the tradeoffs between streamflow restoration and economic welfare loss in a Chicago suburban county, McHenry County. The multiobjective optimization was conducted at both county and municipality levels. An evolutionary algorithm, the nondominated sorting genetic algorithm, was used to solve the optimization model and to identify the tradeoff curve (Pareto frontier). Comparing municipal Pareto frontiers shows spatially heterogeneous costs of preserving streamflow through various shadow prices and also the different capacities of restoring streamflow. The results include discussion of the shapes of the Pareto frontier, the sensitivity of the pumping boundary constraints, and return flow coefficients. It is concluded that the multiobjective optimization model provides a useful framework to consider conflicting objectives in a typical environmental management and planning process, and that the findings can help decision-makers and planners to formulate effective groundwater pumping strategies. [ABSTRACT FROM AUTHOR]

In a highway asset maintenance management system involving more than one highway asset type, identifying an equitable optimal allocation of available budget to individual asset systems is a major challenge. This is because the following outcomes are desirable: (1) the maintenance needs of all assets are adequately addressed, (2) the objectives of individual asset systems are optimally satisfied in an equitable manner, and (3) the objectives of the overall highway asset system are achieved optimally. To achieve these desirable outcomes, this paper proposes a two-stage approach in solving the dual-level multiasset, multiobjective pavement network maintenance optimal budget allocation problem. Stage I of the approach analyzes the individual multiobjective asset systems independently to establish for each a family of optimal Pareto solutions. Minimization of maintenance cost is selected as a common objective for the individual asset systems. This serves as the link for interaction with the Stage II analysis. Stage II adopts an optimal algorithm to allocate budget to individual assets by performing a cross-asset trade-off to achieve the optimal budget solution for the given overall system-level objectives. By defining a minimum performance threshold level for each asset type, the Stage II optimization analysis aims to achieve an equitable allocation of budget by maintaining equivalent amounts of performance improvements for the individual asset systems above their respective minimum threshold levels. The conceptual framework of the proposed approach is presented in this paper with a numerical example to illustrate the detailed working of the procedure. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal contraflow lane configuration problem arising in the contraflow lane control strategy that has been implemented successfully in practice. The problem is formulated as a bilevel programming model in which the upper level problem is a binary integer programming formulation that aims to minimize the total travel time of a study area, while the lower level problem is a microscopic traffic simulation model that can simulate the dynamic reaction of the drivers resulting from a contraflow lane configuration scheme. A microscopic traffic simulation model is adopted in this study because it is easily handled by traffic engineers. Such an adoption results in inexistence of analytical expression of the objective function in the upper level problem. Accordingly, some conventional analytical solution methods for solving integer programming problems are no longer available for the proposed model. Therefore, this paper develops a variation of genetic algorithm that embeds with the microscopic traffic simulation model as well as a chromosome repairing procedure to find an optimal contraflow lane configuration solution. A case study in Singapore is carried out to evaluate the proposed methodology, in which PARAMICS as the microscopic traffic simulation model is applied. [ABSTRACT FROM AUTHOR]

Recent studies have successfully introduced genetic algorithms (GA) to identify the important failure modes of complex structures and quantify their contributions to the reduction of the reliability of structural systems. In these studies, the efficiency of traditional GA techniques was substantially improved by incorporating linkage-learning operators that were used to explore relations among the random variables controlling the safety of a structural system. However, the currently used linkage learning methods were found to be either too complex for easy implementation in routine reliability analyses or too narrowly focused on finding the global optimal solution and thus reduced the capacity of GA to identify local optima. Other techniques especially suitable for exploring relations and linkages among data sets are known as data mining (DM) algorithms. One of the most popular and successful DM tools available in the computer science literature is the a priori algorithm. The purpose of this paper is to propose a hybrid algorithm that would combine the benefits of the pattern identification ability of the a priori DM algorithm to the capability of GA operators to explore new significant search domains. The implementation of the shredding operator and its ability to reduce the computational effort through its self-learning process will further lead to the development of an efficient and robust structural reliability analysis algorithm. This paper will demonstrate that the proposed algorithm will significantly reduce the computational effort associated with determining the probabilistically dominant failure modes of structural systems. Examples are provided to verify the high efficiency and accuracy of the proposed hybrid data mining/genetic shredding algorithm for failure mode exploration, identification, and exploitation. [ABSTRACT FROM AUTHOR]

Optimal coordination of traffic signals requires proper activation of timing plans to match current traffic conditions. One of the greatest challenges in configuring a traffic responsive plan selection control mode is how to select only n number of timing plans (restricted by traffic controller memory limitations) to address possibly all traffic conditions/states that can be encountered in the field, for a specific site, but most importantly, how to associate each of these traffic conditions to one of the n timing plans stored in the traffic controller. This paper uses a multiobjective nondominated sorting genetic algorithm to (1) select the best n timing plans to be stored in the controller and (2) group traffic states into m number of clusters, and associate each of the m clusters with one of the stored timing plans. A new performance measure, degree of detachment, developed by the writers to address the clustering requirements, is described in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a study that uses an innovative numerical method called the “genetic algorithm” for material parameter optimization in constitutive modeling. The paper introduces a new scheme, a fitness function, to estimate the error of predicted behavior due to a given set of material parameters. Optimization efficiency of the proposed fitness function is analyzed by changing the selected genetic algorithm parameters. The genetic algorithm and the new fitness function were found to be capable of optimizing material parameters of complex constitutive models. [ABSTRACT FROM AUTHOR]

In a real construction project, the duration and cost of each activity could change dynamically as a result of many uncertain variables, such as weather, resource availability, productivity, etc. Managers/planners must take these uncertainties into account and provide an optimal balance of time and cost based on their own experience and knowledge. In this paper, fuzzy sets theory is applied to model the managers’ behavior in predicting time and cost pertinent to a specific option within an activity. Genetic algorithms are used as a searching mechanism to establish the optimal time–cost profiles under different risk levels. In addition, the nonreplaceable front concept is proposed to assist managers in recognizing promising solutions from numerous candidates on the Pareto front. Economic analysis skills, such as the utility theory and opportunity cost, are integrated into the new model to mimic the decision making process of human experts. A simple case study is used for testing the new model developed. In comparison with the previous models, the new model provides managers with greater flexibility to analyze their decisions in a more realistic manner. The results also indicate that greater robustness may be achieved by taking some risks. This research is relevant to both industry practitioners and researchers. By incorporating the concept of fuzzy sets, managers can represent the range of possible time–cost values as well as their associated degree of belief. The model presented in this paper can, therefore, support decision makers in analyzing their time–cost optimization decision in a more flexible and realistic manner. Many novel ideas have also been incorporated in this paper to benefit the research community. Examples of these include the use of fuzzy sets theory, nonreplaceable front concept, utility theory, opportunity cost, etc. With suitable modifications, these concepts can be applied to model to other similar optimization problems in construction. [ABSTRACT FROM AUTHOR]

In past research, several versions of hybrid genetic algorithm–simulation methodology have been proposed for scheduling of multiple lane closures that aims to minimize a network’s total traffic delay. The genetic algorithm is used as a search engine for generation of lane closure schedule, while a microscopic traffic simulation model is employed to calculate the total network travel time under each lane closure scenario. A difficulty in implementing this methodology practically is the long computing time required, due to the many simulation runs needed to evaluate the average total network travel time of each feasible schedule. This paper applies the precondition technique, standard error criterion, and termination criterion to reduce the number of necessary simulation runs. As a further improvement, traffic simulations are distributed in different processors of a multiprocessor machine. To further reduce the computing time, a two-stage hybrid genetic algorithm methodology has been proposed in this paper. This two-stage methodology consists of a hybrid genetic algorithm–traffic assignment methodology as the first stage, followed by a hybrid genetic algorithm–distributed simulation methodology as the second stage. The traffic assignment model is used to replace the traffic simulation model in the estimation of total network travel time in stage 1. The applications of the improvement techniques have been demonstrated through a hypothetical problem involving 20 lane closure requests in a network consisting of 986 links, 397 nodes, and 22 origin-destination zones. Together, these improvement techniques contributed to up to 87% reduction in waiting time for a solution of the example problem. [ABSTRACT FROM AUTHOR]

A computational framework is proposed as an automated design cycle for the optimal seismic design of steel frames with self-centering connections. This paper demonstrates the approach by considering steel frames retrofitted with posttensioned energy dissipating (PTED) connections. A multiphase genetic algorithm is applied for discrete optimization to determine robust steel moment-resisting frame (MRF) designs incorporating PTED connections. Nonlinear dynamic time history finite-element analyses are employed to evaluate potential designs. For the case studies in this paper, results indicate that the performance of the MRF with optimal PTED connections exceeds that of MRFs with conventional moment-resisting connections for a specified seismic environment. This suggests that the PTED connections are a promising alternative to conventional welded moment-resisting connections in seismic regions. Furthermore, the developed computational framework is shown to provide an attractive approach for the design of MRFs with PTED connections and may be employed for the optimal design of other self-centering systems. [ABSTRACT FROM AUTHOR]

This work presents a postoptimization methodology for refining the solutions found by adaptive search algorithms used in the design of large water distribution networks. The approach uses two heuristics to search for an optimal combination of pipes that, after a reduction of their diameters, will maximize cost savings while continuing to meet design constraints. Adaptive search methods are often used to design urban water distribution networks when the number of pipes in the network is insignificant. For complex, real-world networks, however, such methods are computationally demanding, and they have difficulty finding near-global optima. To identify a solution as close to the global optimum (and in which no pipe can be reduced without violating pressure constraint), requires a high-speed computer potentially running for a long time and also probably some good fortune. The postoptimization approach presented in this paper is shown to be an efficient complement to heuristic search algorithms used in the design of real-world networks. In a network created with the aid of a genetic algorithm, the proposed heuristics found that 4.37% of the pipes with a diameter greater than the minimum could be further reduced without causing hydraulic failure. [ABSTRACT FROM AUTHOR]

This paper presents a procedure to identify the damage of multistory shear structure from sparse modal information. Practically, it is difficult and expensive to measure modal response at many locations. A method has been proposed to compute the full modal information of a particular mode simply by knowing the frequency value of that mode. Then the concept of the residual force vector along with genetic algorithm has been implemented to get the location and extent of damage. Frequencies at different number of modes with and without corresponding partial mode shape is used as input to the model and results are compared to find out minimum number of modal information and their locations required for the best result. Experimental data are simulated numerically by solving eigen value problem of the damaged structure with inclusion of random noise on the vibration characteristics. Reliability of the procedure has been shown by various examples of multistory structure with different damaged situations. The model is also validated with some published results in this area to show the efficacy of the methodology. [ABSTRACT FROM AUTHOR]

The increased interest in the development and application of large-scale or regional microsimulation transportation models has brought to the forefront the challenges associated with estimating the dynamic demand information needed to run such models. This paper develops a computational-based approach for estimating or adjusting dynamic origin-destination matrices for regional microsimulation models on the basis of hourly traffic counts. The proposed approach, while based on genetic algorithms (GA), includes a special module, called Plan Analyzer, to guide the search process in an intelligent way. This results in a customized algorithm for the problem that can be regarded as an example of a guided genetic algorithm (GGA). To cut down on execution time, a distributed implementation of the algorithm is adopted, and several software design procedures are developed to deal with the demanding memory requirements of the problem. To demonstrate the effectiveness of the algorithm, the Transportation Analysis and Simulation System (TRANSIMS) model, a microsimulation platform designed for regional simulations, is used to model two test networks, a synthetic grid network and a realistic regional model of Chittenden County, Vermont. The GGA is then utilized to estimate the dynamic demand for those two models on the basis of hourly traffic count information. The results clearly demonstrate the effectiveness of the GGA in dramatically reducing the average absolute error (AAE) between the simulated and field counts, and in closely estimating the 'true' demand, which was known in this research by virtue of how the case studies were designed. The results also show that the developed GGA significantly outperforms standard GAs. [ABSTRACT FROM AUTHOR]

Designing baseline or vertical alignment in railways greatly depends on the designer's experience. An inaccurate design imposes unreasonable costs during construction and operations. This paper presents a program to find the best vertical alignment for a track with a given horizontal layout. First, the vertical alignment is formulated using mathematical equations, and then an algorithm is developed to produce its geometric configuration. Construction and operating costs, which are sensitive to gradient changes, are expressed as vertical alignment variables and included in the model. A direct search method is employed to solve the objective function, and a genetic algorithm encodes solution algorithms in a program, whereas certain heuristic operators are utilized to cover operational situations. The program also provides railway engineers and planners with practical information on the resulting alignment such as time and speed graph, total traction force, and block capacity. Moreover, the model is evaluated using a real example to verify the algorithms' ability to find the optimal alignment. [ABSTRACT FROM AUTHOR]

Concrete beams reinforced with glass-fiber reinforced polymer (GFRP) bars exhibit large deflections in comparison with steel-reinforced concrete beams because of the low modulus of elasticity of GFRP bars. This paper proposes new equations for estimating the effective moment of inertia of FRP-reinforced concrete beams on the basis of the genetic algorithm and experimental results. Genetic algorithm is used to optimize the error function between experimental and analytical responses. In the experimental part of the study, nine beam specimens were manufactured and tested. In addition, the results of 55 beam specimens tested by other researchers were also used. The effects of elastic modulus of FRP bars, reinforcement ratio, and the level of loading on the effective moment of inertia are taken into account. The proposed equations are compared with different code provisions and previous models for predicting the deflection of FRP-reinforced concrete beams. The values calculated using the proposed equations are also compared with different test results. The experimental results correlated well with the values predicted using the proposed equations, especially in the cases of high reinforcement ratios and high levels of loading. [ABSTRACT FROM AUTHOR]

An accuracy-based learning classifier system (XCS), as described in a companion paper (Part I: Design), was developed and evaluated to produce operational rules for canal gate structures. The XCS was applied together with a genetic algorithm and an unsteady hydraulic simulation model, which was used to predict responses to gate operation rules. In the tested cases, from 100 to 2,000 XCS simulations, each involving thousands of hydraulic simulations, were required to produce satisfactory rules. However, the overall fitness of the set of rules increased monotonically as XCS simulations progressed. Initial fitness started at an arbitrary value, and rules increased in strength by better achieving operational objectives during the training process. Fewer XCS iterations were required to increase the fitness as the rule population evolved. Calculated water depths approached the respective target depths for variable water delivery demand through turnout structures in the simulated canal systems. The water depth achieved stabilization inside a dead band of ± 8% of the target depth after applying different turnout demand hydrographs to each reach. The calculated depth was inside the dead band 92% of the time in Reach 1, and 73% of the time in Reach 2 for the constant supply experiment. The water depth was inside the dead band 100% of the time in Reach 1, and 76% of the time in Reach 2 for the variable-supply experiment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field’s subdisciplines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises. [ABSTRACT FROM AUTHOR]

In this paper, a bilevel programming model for the bus frequency design is presented, which determines the optimal bus frequencies aiming to minimize the total travel time of passengers subject to the constraint on the overall fleet size of each company by accounting for the route choice behaviors of the users. The objective of the lower level is to assign transit trips to bus route network based on optimal strategy. In the upper level, bus frequencies of routes are optimized as a result of passenger assignment. An iterative approach, which consists of a genetic algorithm and a label-marking method, is used to solve the bilevel model. Finally, the model and the algorithms are illustrated with two test examples. The results show that the optimization can improve the local service level of one company, and the proper integration of several companies probably improves the efficiency of resources and the service level of the whole transit system. [ABSTRACT FROM AUTHOR]

The suitability of genetic algorithms (GAs) for the optimization of water distribution systems (WDSs) has been demonstrated extensively. However, despite many years of application in many different fields, the selection of the GA parameters remains a difficult and time consuming task. In this paper, two methodologies that do not require trial-and-error GA parameter calibration have been tested on a WDS optimization problem to determine their suitability for application in the water resources field and to assess their ability in locating near-optimal solutions. The results indicate that both approaches located solutions that were significantly better than a GA using typical parameter values, while the methodology based on convergence of the GA population located the best solutions overall. This method can be easily applied to assist GA users in identifying suitable GA parameters without requiring a time consuming trial-and-error approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimal pressure management in water distribution systems through the introduction and regulation of pressure reducing valves. Reduction in pressure is aimed at controlling water leakages which, being in some cases a high proportion of the total volume supplied, are nowadays one of the major concerns for water utilities. The determination of the number, location, and setting of such valves is formulated as a two criteria optimization problem and is solved with multiobjective genetic algorithms. In particular, the first criterion is represented by the minimization of the number of valves, and the second is the minimization of the total leakage in the system, when maintaining the required pressure at each node. The great advantage of the multiobjective approach resides in the fact that, in one run, several trade-off alternatives are found, thus providing the set of the optimal solutions with a different level of compromise between the conflicting objectives. At the same time, data necessary for practical choice and operation of pressure reducing valves may be determined. [ABSTRACT FROM AUTHOR]

This paper describes a model developed for optimizing water supply plans for water customers when a water network is partially operational due to the destruction of water networks by malicious attacks or any catastrophic events. The model uses the information regarding the priority of water customers in emergency situations, their functional sensitivities to water supply, and the dynamic nature of water demand patterns. The solutions generated from the model are water rationing plans which can guarantee at least a short-term water supply to water customers during any 24-h period. A genetic algorithm based program linked with a hydraulic solver (EPANET 2.0) is developed to find optimal water rationing plans. A case study indicates that the proposed model can successfully generate water rationing plans to reduce the consequences while meeting hydraulic constraints in water networks. The model developed in this study is also applicable to extreme water supply shortage situations due to severe drought or nonintentional incidents such as major pump failures and main breaks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The short-term optimal hydrogeneration planning is a complicated nonlinear constrained optimization problem with water delay time. To overcome the shortcomings of a standard genetic algorithm, this paper proposes a new real-value encoding self-adaptive chaotic genetic algorithm to solve this problem, which designs a new crossover operator in light of probability distribution function and a self-adaptive chaotic mutation operator combined chaotic dynamic character with artificial neural network theory. Constraints can be dealt with by using a simple direct comparison penalty function method without the need of any penalty coefficient. The feasibility of the proposed method is demonstrated for short-term generation scheduling of two test hydrosystems and the test results are compared with those obtained by the standard genetic algorithm in terms of solution quality and convergence characteristic. The simulation results show that the proposed method is capable of obtaining higher quality solutions. [ABSTRACT FROM AUTHOR]

In this paper, based on the Darwinian and Lamarckian evolution theories, three hybrid genetic programming (GP) algorithms integrated with different local search operators (LSOs) are implemented to improve the search efficiency of the standard GP. These three LSOs are the genetic algorithm, the linear bisection search, and the Hooke and Jeeves method. A simple encoding method is presented to encode the GP individuals into the expressions that can be recognized by the different LSOs. The implemented hybrid GP algorithms are applied to identify the excitation force acting on the structures from the measured structural response, which is an important type of inverse problem in structural dynamics. Illustrative examples of a frame structure and a multistory building structure demonstrate that, compared with the standard GP, the hybrid GP algorithms have higher search efficiency which can be used as alternate global search and optimization tools for other engineering problem solving. [ABSTRACT FROM AUTHOR]

This paper presents a new method for applying reliability-based design approaches to slope stability analysis. In this method the soil properties are considered to be random variables. The factor of safety of the slope is found using Bishop’s simplified method for noncircular slip surfaces. By considering the variability of the soil properties, the probability of failure is determined from the reliability index (β). The minimization problem (determination of the lowest β value for the range of variables and possible slip surfaces considered) is solved using a genetic algorithm approach, which simultaneously locates the critical slip surface and determines the reliability index. The performance of the new method is compared to some existing reliability approaches when applied to case histories of slope failures from the geotechnical literature. The new approach is seen to provide reasonable and consistent estimates of the reliability index and critical slip surface. [ABSTRACT FROM AUTHOR]

This paper presents the genetic algorithm (GA) solution of a gray nonlinear water environment management model we developed for the Liming River basin in Daqing, China to improve the water environment management. The model has been developed by both optimizing the operation of wastewater treatment plants and making full use of assimilative capacity of the river so that the optimum integration of these two measures can keep the water quality in the Liming River basin up to a satisfactory standard at a reasonable cost. It can be used as an example to illustrate the potential application of a GA-based gray nonlinear programming in the field of water environment management. [ABSTRACT FROM AUTHOR]

Even though several genetic algorithm (GA)-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, such methods are computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. Furthermore, the successful implementation of GA-based optimization algorithm requires a cumbersome routine through trial-and-error for tuning the GA parameters that are different depending on each problem. Therefore, to overcome these difficulties, a high-performance GA is developed in the form of a distributed hybrid genetic algorithm for structural optimization, implemented on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consists of a μ-GA running on a master computer and multiple simple GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computation time required for GA-based optimization can be drastically reduced and the problem-dependent parameter tuning process can be avoided. [ABSTRACT FROM AUTHOR]

Homeland security challenges civil engineers to develop solutions to protect critical infrastructure systems from intentional attacks. The water infrastructure system is one of eight critical infrastructures defined in the report by the President’s Commission on Critical Infrastructure Protection (PDD 1998). This paper offers a model for an operational response strategy to mitigate consequences of intentional physical attacks on the water infrastructure system. An intentional physical attack occurs when a water infrastructure facility is nonfunctional because of physical destruction of the facility by individuals or groups with malicious intentions. Three indices are presented in this paper to measure the consequences of intentional physical attacks: (a) the degree of the disruption of critical infrastructure services, (b) economic loss, and (c) the number of people affected. Then, a multiobjective genetic algorithm is used to find solutions to affect minimum consequences among these indicators. Evaluation of the model using two water infrastructure networks indicates that the genetic algorithms can successfully identify Pareto-optimal solutions to reduce the negative consequences of different attack scenarios. These solutions represent a pool of strategies to be incorporated in emergency plans. In addition, the modified nondominated sorting genetic algorithms (NSGA-II) developed in this study can significantly improve the quality of the solution early in the evolution process. This can be very critical when the time available to obtain mitigation plans is very limited. [ABSTRACT FROM AUTHOR]

A genetic algorithm (GA)-based method for the least-cost design of looped pipe networks for various levels of redundancy is presented in this paper. Redundancy constraints are introduced in the optimization model by considering the number of pipes assumed to be out of service at any one time. Using this approach, trade-off relationships between cost and redundancy are developed. The GA-based approach is computationally intensive, and implementations on a custom fault-tolerant distributed computing framework, called Vitri, are used to satisfy the computational requirements. The design methodology is applied to two water distribution networks of different sizes, and a comparison of the performance of the distributed GAs for the design problems is also presented. We conclude that a GA-based approach to obtaining cost-effective, redundant solutions for the least-cost design of looped pipe networks can be effectively used on a heterogeneous network of nondedicated workstations. [ABSTRACT FROM AUTHOR]

This paper presents a nondestructive global damage detection and assessment methodology based on the changes in frequencies and mode shapes of vibration of a structural system. The method is applied at an element level using a finite-element model. According to continuum damage mechanics, damage is represented by a reduction factor of the element bending stiffness. A nonclassical optimization approach involving the use of genetic algorithms (GAs) is proposed to localize damaged areas of the structure. The method has been verified by a number of damage scenarios for simulated beams and by using directly experimental data from the vibration tests of a beam. It is shown that the proposed GA yields a suitable damage location and severity detection while introducing numerous advantages compared to classical methods. The influence of noise in the modal data has also been considered. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for the optimal design of water distribution systems based on genetic algorithms. The objective of the optimization is to minimize the capital cost, subject to ensuring adequate pressures at all nodes during peak demands. The proposed method is novel in that it involves the use of a pipe index vector to control the genetic algorithm search. The pipe index vector is a measure of the relative importance of pipes in a network in terms of their impact on the hydraulic performance of the network. By using the pipe index vector it is possible to exclude regions of the search space where impractical and infeasible solutions exist. By reducing the search space it is possible to generate feasible solutions more quickly and hence process much healthier populations than would be the case in a standard genetic algorithm. This results in optimal solutions being found in a fewer number of generations resulting in a substantial saving in terms of computational time. The method has been tested on several networks, including networks used for benchmark testing least cost design algorithms, and has been shown to be efficient and robust. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Identifying contaminant sources in groundwater is important for developing effective remediation strategies and identifying responsible parties in a contamination incident. Groundwater source identification problems require solution of an inverse problem. Gradient-based local optimization approaches are among the most popular approaches for solving these inverse problems. While these methods are sometimes appropriate, they are not effective for problems that contain several local minima and for problems where the decision space is highly discontinuous or convoluted. For these types of problems, heuristic global search approaches such as genetic algorithms (GAs) are more effective. But methods such as GAs are inefficient for fine-tuning solutions once a near global minimum is found. For problems that contain several local minima, a hybrid approach starting with a global method and then fine-tuning with a local method may be more attractive, especially if the decision space is reasonably well behaved near the solution. In this paper, we compare several popular optimization methods for solving a simple groundwater source identification problem and show that hybrid GA-local search (GA-LS) approaches are generally more effective than using stand alone versions of each method. Some variants of the GA-LS approaches are then implemented on a parallel supercomputer to solve a more complex three-dimensional problem. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reducing both project cost and time (duration) is critical in a competitive environment. However, a trade-off between project time and cost is required. This in turn requires contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Although several analytical models have been developed for time-cost optimization (TCO), they mainly focus on projects where the contract duration is fixed. The optimization objective in those cases is therefore restricted to identifying the minimum total cost only. With the increasing popularity of alternative project delivery systems, clients and contractors are targeting the increased benefits and opportunities of seeking an earlier project completion. The multiobjective model for TCO proposed in this paper is powered by techniques using genetic algorithms (GAs). The proposed model integrates the adaptive weights derived from previous generations, and induces a search pressure toward an ideal point. The concept of the GA-based multiobjective TCO model is illustrated through a simple manual simulation, and the results indicate that the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]