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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 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 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]

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]

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]