Search

In this paper, a Cellular Automata based simulation-optimization approach is proposed for the optimal design of household sewer networks. A two-phase CA is used as the optimization tool while the EPA's storm water management model (SWMM) is used as the simulator. A splitting method is first used to redefine the sewer network design problem in terms of two simpler sub-problems with diameters and nodal elevations of each pipe as decision variables which are iteratively solved using CA methods in two-stage manner until convergence is achieved. Each CA uses a separate ad-hoc local rule to update the cell state of the corresponding problem derived using engineering judgment and the hydraulic principles of the sewer flow. The proposed method is applied to some benchmark sewer networks and the results are presented and compared to those of the existing methods. Results indicates the efficiency and effectiveness of the proposed methods compared to the existing methods. [ABSTRACT FROM AUTHOR]

In this study, the impact of various rescaling approaches in the framework of data fusion is explored. Four different soil moisture products (Advanced Scatterometer; Advanced Microwave Scanning Radiometer for EOS, AMSR‐E; Antecedent Precipitation Index; and Global Land Data Assimilation System‐NOAH) are fused. The systematic differences between products are removed before the fusion utilizing various rescaling approaches focusing on different methods (regression, variance/cumulative distribution function (CDF) matching, multivariate adaptive regression splines, and support vector machines based), stationarity assumptions (constant or time‐varying rescaling coefficients), and time‐frequency techniques (periodic or nonperiodic high‐ and low‐frequency components). Given that statistical descriptions (e.g., standard deviation and correlation coefficient) of reference data sets are utilized in rescaling approaches, the precision of the selected reference data set also impacts the final fused product precision. Experiments are validated over 542 soil moisture monitoring sites selected from the International Soil Moisture Network data sets between 2007 and 2011. Overall, results highlight the importance of reference data set selection—particularly that a more precise reference product yields a higher precision fused soil moisture product. This conclusion is sensitive neither to the number of fused products nor the rescaling procedure. Among rescaling approaches, the precision of fused products is most affected by the choice of rescaling stationary assumption and time‐frequency decomposition technique. Variations in rescaling methods have only a small impact on the precision of pair fused products. In contrast, utilizing a time‐varying stationary assumption and nonperiodic decomposition technique produces correlation improvements of 0.07 [−] and 0.02 [−], respectively, versus the other widely implemented rescaling approaches. Key Points: Precision of the fused products obtained using suitable linear rescaling methods is similar to the ones obtained using nonlinear methodsSelection of better reference data set yields more precise fused productApplication of a smooth‐deviance decomposition rescaling technique improves correlations [ABSTRACT FROM AUTHOR]

In this paper, a weighted cellular automata (CA) is proposed to solve bi-objective reservoir operation optimisation problem considering two objectives of water supply and hydropower production. A mathematically derived updating rule is used contributing to the efficiency of the proposed CA method. The updating rule of the problem is derived by converting the bi-objective problem to a single-objective problem using the well-known weighting method. The proposed method is used to operate the Dez reservoir in Iran over various operation periods of 60, 120, 240 and 480 months to test the performance of the method for operational problems of different scales. Performance of the method is also compared with that of a non-dominated sorting genetic algorithm (NSGAII) as one of the most popular multi-objective evolutionary algorithms. The results indicate that the proposed method is highly efficient compared to the NSGAII while producing comparable results. This is in line with the early findings of superior efficiency and comparable effectiveness of the CA method with the existing evolutionary algorithms for single objective optimisation problems. [ABSTRACT FROM AUTHOR]

In this paper, the incremental solution building capability of Ant Colony Optimization Algorithm (ACOA) is exploited using a Tree Growing Algorithm (TGA) augmented with the efficiency of Nonlinear Programming (NLP) methods leading to a hybrid ACOA-TGA-NLP algorithm for the effective layout and pipe size optimization of pumped/gravitational sewer networks. Solution of layout and pipe size optimization of sewer network requires the determination of pipe locations, pipe diameters, average pipe cover depths, drop and pump heights minimizing the total cost of the sewer network subject to operational constraints. The resulting problem is a highly constrained Mixed-Integer Nonlinear Programming (MINLP) problem presenting a challenge even to the modern heuristic search methods. In the proposed method, the TGA is used to construct feasible tree-like layouts out of the base layout defined for the sewer network, the ACOA is used to optimally determine the pipe diameters of the constructed layout, and finally NLP is used to determine the pipe slopes from which the remaining characteristics of the network such as pump/drop locations and heights are determined. In the NLP stage of the model, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily enforced as box constraint of the NLP solver leading to a considerable reduction of the search space size. The proposed hybrid ACOA-TGA-NLP has two significant advantages over other available methods. First, this method can be used for both pumped and gravitational sewer networks. Second, the computational effort is significantly reduced compared to alternative methods. Another method is also proposed here in which the layout of the network is determined by an ad-hoc method based on engineering judgment while the component design of the network is carried out by ACOA-NLP method as defined above. Proposed hybrid methods are used to solve a benchmark example from the literature and a hypothetical test example and the results are presented and compared with those produced by the existing methods such as SPST-DDDP, SDM-DDDP and GA-DDDP. The results indicate the efficiency and effectiveness of the proposed methods and in particular the ACOA-TGA-NLP method. In fact, the optimal solution of ACOA-TGA-NLP is 149, 64.1, 22.2 and 13.6% cheaper than those of ACOA-NLP, SPST-DDDP, SDM-DDDP and GA-DDDP methods, respectively, for the benchmark text example. Furthermore, ACOA-TGA-NLP yields a solution 80% cheaper than that of ACOA-NLP method for hypothetical test example. [ABSTRACT FROM AUTHOR]

A Mixed formulation of Discrete Least Squares Meshless (MDLSM) as a truly meshfree method is presented in this paper for solving both linear and non-linear propagation problems. In DLSM method, the irreducible formulation was deployed which needs to calculate the costly second derivatives of the MLS shape functions. In the proposed MDLSM method, the complex and costly second derivatives of shape functions are not required. Furthermore, using the mixed formulation, both unknown parameters and their gradients are simultaneously obtained circumventing the need for post-processing procedure performed in irreducible formulation to calculate the gradients. Therefore, the accuracy of gradients of unknown parameters is increased. In MDLSM method, the set of simultaneous algebraic equations are built by minimizing a least squares functional with respect to the nodal parameters. The least squares functional defined as the sum of squared residuals of the differential equation and its boundary condition. The proposed method automatically leads to symmetric and positive-definite system of equations and, therefore, is not subject to the Ladyzenskaja-Babuska-Brezzi (LBB) condition. The proposed MDLSM method is validated and verified by a set of benchmark problems. The results indicate the ability of proposed method to efficiently and effectively solve the linear and non-linear propagation problems. [ABSTRACT FROM AUTHOR]

This paper presents a novel cellular automata Approach as the solution to reliability-based reservoir hydropower operation problems. The method is based on the observation that a low value of the penalty parameter would lead to partial enforcement of the constraints. Therefore, in this method, the constraints of the chance-constrained operation problem, namely operational and reliability constraints, are dealt with differently. A high enough value of the penalty parameter is used for the first set while a lower than enough value is used for the second set, leading to strong enforcement of the first set of the constraint and partial fulfillment of the second set. Since the proper value of the penalty parameter to be used for the reliability constraints is not known a priori, an adaptive method is proposed to find the proper value. The proposed model is used for operation of Dez reservoir in Iran and the results are presented and compared with those of a Genetic Algorithm. Hydropower operation is considered over short, medium, and long-term periods to demonstrate the efficiency and effectiveness of the proposed method for problems of different scales. The proposed model has proven to produce results superior to the results of G A with much reduced computational effort. [ABSTRACT FROM AUTHOR]

An adaptive hybrid genetic algorithm (GA) and cellular automata (CA) method is proposed for solving implicit stochastic optimization of reservoir operation problems. The method is based on a decomposition approach in which the reliability constraints are handled with GA, whereas the resulting deterministic problem is solved with a CA model. Two versions, binary and integer GA, were employed for handling the reliability constraints of the problem. In the first one, GAwas used to determine the success/failure pattern of the operation, whereas in the latter, only failure periods were determined with GA. The proposed method was used for monthly water supply and hydropower operation of an existing reservoir and the results are presented and compared with those of a GA model. To demonstrate the efficiency and scale independency of the model, short-term, medium-term, and long-term operations are considered assuming different target reliabilities. Comparison of the results with those of a GA model shows the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid mixed-integer linear programming-linear programming-linear programming (MILP-LP-LP) methodology for simultaneous optimal design and operation of unconfined groundwater utilization systems. The proposed model is an extension to the earlier LP-LP model introduced by the authors for the optimal design and operation of confined groundwater utilization systems. The proposed model can be used to minimize the total cost, including the well drilling, pump installation cost and energy cost, of utilizing a two-dimensional unconfined aquifer under both steady-state and transient flow conditions. The solution of the problem is defined by the well numbers and location, well drilling depth and the corresponding pumping rates, satisfying a downstream demand, lower/upper bound on the pumping rates, and lower/upper bound on the water level drawdown in the wells. A discretized version of the differential equation governing the aquifer is first embedded into the model formulation as additional constraints. The resulting mixed-integer non-linear programming problem is then decomposed into three sub-problems with different sets of decision variables, namely, hydraulic head, well numbers and locations and their pumping rates, and well depths. Startingwith a set of randomvalues for all decision variables, the three sub-problems are solved assuming fixed values for the other variables. This process is iterated until convergence is achieved. [ABSTRACT FROM AUTHOR]

In this paper, the Mixed Discrete Least Squared Meshless (MDLSM) method is used for solving the quadratic partial differential equations (PDEs). In MDLSM method the domain is discretized only with nodes and a minimization of a least squares functional is carried out. The least square functional is defined as the sum of the residuals of the governing differential equation and its boundary condition at the nodal points. In MDLSM, the main unknown parameter and its first derivatives are approximated independently with the same Moving Least Squares (MLS) shape functions. The solution of the quadratic PDE does not, therefore, require the calculation of the complex second order derivatives of MLS shape functions. Furthermore, both the Neumann and Dirichlet boundary conditions can be treated and imposed as a Dirichlet type boundary condition which is applied using a penalty method. The accuracy and efficiency of the MDLSM method are tested against three numerical benchmark examples from one-dimensional and two-dimensional PDEs. The results are produced and compared with the irreducible DLSM method and exact analytical solutions indicating the ability and efficiency of the MDLSM method for the efficient and effective solution of quadratic PDEs. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, cellular automata based hybrid methods are proposed for the optimal design of sewer networks and their performance is compared with some of the common heuristic search methods. The problem of optimal design of sewer networks is first decomposed into two sub-optimization problems which are solved iteratively in a two stage manner. In the first stage, the pipe diameters of the network are assumed fixed and the nodal cover depths of the network are determined by solving a nonlinear sub-optimization problem. A cellular automata (CA) method is used for the solution of the optimization problem with the network nodes considered as the cells and their cover depths as the cell states. In the second stage, the nodal cover depths calculated from the first stage are fixed and the pipe diameters are calculated by solving a second nonlinear sub-optimization problem. Once again a CA method is used to solve the optimization problem of the second stage with the pipes considered as the CA cells and their corresponding diameters as the cell states. Two different updating rules are derived and used for the CA of the second stage depending on the treatment of the pipe diameters. In the continuous approach, the pipe diameters are considered as continuous variables and the corresponding updating rule is derived mathematically from the original objective function of the problem. In the discrete approach, however, an adhoc updating rule is derived and used taking into account the discrete nature of the pipe diameters. The proposed methods are used to optimally solve two sewer network problems and the results are presented and compared with those obtained by other methods. The results show that the proposed CA based hybrid methods are more efficient and effective than the most powerful search methods considered in this work. [ABSTRACT FROM PUBLISHER]

The particle swarm optimisation algorithm is a global optimisation method proposed and mainly used for continuous optimisation problems. The method shows interesting features, in particular fast convergence characteristics. It does, however, lack sufficient exploration, especially when the grouping of the swarm starts leading to sub-optimal solutions when solving difficult problems. This paper introduces two mutation mechanisms to balance the exploitative characteristics of the algorithm. The timing of the proposed mutations is designed such that the inherent exploration of the method is not disturbed. The proposed mutated algorithms cannot, therefore, produce inferior results to that of the original method. Furthermore, mutation is only carried out on those particles that are already converged to the global best position and, in effect, are not of any particular use to the collective intelligence of the swarm. The performance of the proposed mutated algorithms is tested against two reservoir operation problems and the results are presented and compared with those of the standard algorithm. The mutated algorithms show improved performance for the examples considered. [ABSTRACT FROM AUTHOR]

In this paper, an efficient and accurate method for the extended period simulation of pipe networks under dynamic loading is proposed. The method uses the modified Euler method to discretise the ordinary differential equation governing the variation of water level in the reservoirs to yield a non-linear algebraic equation in terms of the reservoir head and inflow. These equations are then embedded into the non-linear system of equations describing the steady-state flow in the pipe network to obtain the final system of equations. A Newton-Raphson method of linearisation is used for the equation governing the tank water level variation along with a gradient formulation of the pipe networks. The solution of the resulting system of equations automatically yields the distribution of nodal heads along with the reservoir head at the end of each period. The method uses only one steady-state simulation per period to predict accurately and efficiently the reservoir water elevation. Application of the proposed method does not require special coding and can be easily embedded into the existing pipe network simulation codes. The method is applied to four test problems and the results are presented and compared with those of a conventional extended period simulation method and some other existing methods. The results show that the proposed method is considerably more accurate than the conventional algorithm and more efficient than existing improved methods of dynamic simulation. [ABSTRACT FROM AUTHOR]

A novel cellular-automata approach is developed in this article for the optimal solution of large-scale reservoir-operation problems. The aim of this article is to show how cellular automata can be used for the solution of reservoir-operation problems, and, more importantly, to demonstrate that the method is extraordinarily more efficient and effective than heuristic-search methods. Both penalized and non-penalized versions of the method are proposed and formulated for the solution of water-supply and hydropower reservoir-operation problems. The cells are defined as the discrete points chosen on the operation horizon of the problem and storage volumes are taken as the cell states. The optimization objective functions of the problems are used to derive the updating rule of the problems. In the non-penalized method, the problems constraints are satisfied explicitly by limiting the change in the cell states between one iteration and the next. In the penalized version, however, a penalty method is used to modify the updating rules so that the constraints are automatically satisfied. The proposed methods are used to optimally solve the problem of water supply and hydropower operation of the Dez reservoir in Iran over short, medium, and long operation periods, and the results are presented and compared with those obtained using three heuristic-search methods (genetic algorithms, Ant Colony Optimization algorithms, and Particle Swarm Optimization algorithms). The results show that the cellular-automata method is much more efficient and effective than most powerful search methods for both of the problems considered in this work. Application of the method to multi-reservoir systems is underway, with encouraging early results. [ABSTRACT FROM AUTHOR]

A penalty adapting ant algorithm is presented in an attempt to eliminate the dependency of ant algorithms on the penalty parameter used for the solution of constrained optimization problems. The method uses an adapting mechanism for determination of the penalty parameter leading to elimination of the costly process of penalty parameter tuning. The method is devised on the basis of observation that for large penalty parameters, infeasible solutions will have a higher total cost than feasible solutions and vice versa. The method therefore uses the best feasible and infeasible solution costs of the iteration to adaptively adjust the penalty parameter to be used in the next iteration. The pheromone updating procedure of the max-min ant system is also modified to keep ants on and around the boundary of the feasible search space where quality solutions can be found. The sensitivity of the proposed method to the initial value of the penalty parameter is investigated and indicates that the method converges to optimal or near-optimal solutions irrespective of the initial starting value of the penalty parameter. This is significant as it eliminates the need for sensitivity analysis of the method with respect to the penalty factor, thus adding to the computational efficiency of ant algorithms. Furthermore, it is shown that the success rate of the search algorithm in locating an optimal solution is increased when a self-adapting mechanism is used. The presented method is applied to a benchmark pipe network optimization problem in the literature and the results are presented and compared with those of existing algorithms. [ABSTRACT FROM AUTHOR]

The application of a max–min ant algorithm to the layout and size optimization of pipe networks is described in this paper. The formulation conventionally used for the pipe size optimization of networks with fixed layout is extended to account for the layout determination of the networks. This is achieved by including new constraints regarding the reliability of the network and modifying some of the constraints of the optimization problem. A deterministic concept of reliability is used in which the number of independent paths from source nodes to each of the demand nodes is considered as a measure of reliability. The method starts with a predefined layout which includes all possible links. The method is capable of designing the layout and pipe sizes of water distribution networks of predefined reliability including tree-like and looped networks. It is also shown that a layout optimization of a network followed by size optimization does not lead to an optimal or a near-optimal solution. This emphasizes the need for simultaneous layout and size optimization of networks if an optimal or near-optimal solution is desired. The performance of the method for layout and pipe size optimization of pipe networks is tested against two benchmark examples in the literature and the results are presented. The first example is considered to show the necessity of joint layout and size optimization even for the simple tree networks while the second example is considered to illustrate the efficiency of the proposed method for layout and size optimization of real-world networks with different levels of reliability. [ABSTRACT FROM AUTHOR]

A heuristic method is presented for the simultaneous layout and component size optimization of water distribution networks. The method is based on the engineering concept of reliability in which the number of independent paths from source nodes to each of the consumption nodes is considered as a measure of reliability. The method starts with a predefined maximum layout which includes all possible and useful connections. An iterative design-float procedure is then used to move from the current to a cheaper layout satisfying a predetermined reliability set by the user. This is achieved via identifying the hydraulically least important pipes and floating the one which would lead to the cheapest layout. A pipe is floated by relaxing its minimum diameter constraint requirement so that the optimization process could eliminate the pipe from the layout by assigning a zero value to its diameter if required. An iterative penalty method is used for design purpose at each iteration. Three different floating procedures are developed and their efficiencies are tested. A heuristic method is also developed to convert the continuous pipe size solution to a set of discrete solutions. The performance of the method for layout optimization of pipe networks is tested against the benchmark example in the literature and the results are presented. [ABSTRACT FROM AUTHOR]

Ant algorithms are now being used more and more to solve optimization problems other than those for which they were originally developed. The method has been shown to outperform other general purpose optimization algorithms including genetic algorithms when applied to some benchmark combinatorial optimization problems. Application of these methods to real world engineering problems should, however, await further improvements regarding the practicality of their application to these problems. The sensitivity analysis required to determine the controlling parameters of the ant method is one of the main shortcomings of the ant algorithms for practical use. Premature convergence of the method, often encountered with an elitist strategy of pheromone updating, is another problem to be addressed before any industrial use of the method is expected. It is shown in this article that the conventional transition rule used in ant algorithms is responsible for the stagnation phenomenon. A new transition rule is, therefore, developed as a remedy for the premature convergence problem. The proposed transition rule is shown to overcome the stagnation problem leading to high quality solutions. The resulting ant algorithms are also found to be less sensitive to the sensitivity indexes, requiring less computational effort for the determination of these parameters. The efficiency and effectiveness of the proposed rule and the resulting algorithm is tested on some pipe network optimization benchmark problems and the results are compared with the existing results using ant algorithms and other evolutionary methods. [ABSTRACT FROM AUTHOR]

In this paper, a General Hybrid Cellular Automata (GHCA) model is hybridized with two of the most reliable heuristic search methods, namely Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACOA), for the simultaneous optimal design of layout and size of pumped and/or gravity sewer networks. GHCA model has recently been proposed by the authors for the optimal size determination of the sewer network with fixed layout. The model has been shown to be able to optimally design pumped and/or gravity sewer networks, if required. In proposed hybrid models, the heuristic search algorithms are used to create trial layout for the network while GHCA is used to design the network by determining the pipe diameters, pipe slopes, drop height, and pump height, if required. An ad-hoc engineering based method is used to determine feasible layouts by GA, while a Tree Growing Algorithm (TGA) is used to construct feasible layout using ACOA. The proposed hybrid models are tested against two benchmark sewer networks and the comparison of results to those of some existing methods indicates that proposed models, and in particular the ACOA-GHCA method, are more efficient and effective than some alternative methods for the optimal design of layout and size of sewer networks. [ABSTRACT FROM AUTHOR]