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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]

A novel hybrid genetic algorithm and cellular automata method is presented here for solving reliability-based reservoir operation problems. The method uses a decomposition approach in which the reliability part of the problem is handled with a GA, while the operational side of the problem is solved by a CA method. In the proposed method, a binary genetic algorithm is employed to determine success/failure periods of the operation. A period is considered as a success period if the corresponding gene value is one, and failure period if otherwise. The success/failure pattern suggested by the genetic algorithm is used to explicitly rewrite the reliability constraints of the operation problem in terms of the reservoir releases. A CA method is then used to solve the resulting problem in which the two types of the constraints, namely operational constraints and reliability constraints, are dealt with differently. Operational constraints are applied to all periods, while reliability constraints are only applied to the success periods determined by genetic algorithm. The proposed method is used for monthly water supply and hydropower operation of Dez reservoir in Iran, and the results are presented and compared with those of a GA model. The efficiency and effectiveness of the method are tested for the short-, medium- and long-term operation periods assuming different target reliabilities. Comparison of the results with those of a GA model shows that the proposed method is both more effective and efficient compared to GA. [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]

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]

Stochastic search methods, such as the particle swarm optimization (PSO) algorithm, are primarily directed by two main features - exploration and exploitation. Exploration is the ability of the algorithm to broadly search through the solution space for new quality solutions, whereas exploitation is responsible for refining the search in the neighborhood of the good solutions found previously. Proper balance between these features is sought, to obtain good performance of these algorithms. An explorative mechanism is introduced in this paper to improve the performance of the PSO algorithm. The method is based on introducing artificial exploration into the algorithm by randomly repositioning the particles approaching stationary status. A velocity measure is used to distinguish between flying and stationary particles. This can be sought as a sudden death followed by a rebirth of these particles. Two options are tested for the rebirthing mechanism, which are (i) clearing and (ii) keeping the memory of rebirthing particles. The global best particle is exempted from rebirthing process so that the most useful of the swarm’s past experiences is not lost. The method is applied to a benchmark storm water network design problem and the results are presented and compared with those of the original algorithm and other methods. The proposed method, though simple, is shown to be very effective in avoiding local optima, leading to an improved version of the algorithm at no extra computational effort. Les méthodes de recherche stochastiques telles que l’algorithme d’optimisation par essaim de particules (« PSO ») sont gérées principalement par deux caractéristiques majeures, l’exploration et l’exploitation. L’exploration est la capacité de l’algorithme à rechercher l’espace de solution de manière grossière pour trouver de nouvelles solutions de qualité alors que l’exploitation est responsable de préciser la recherche dans l’entourage des bonnes solutions trouvées précédemment. Un bon équilibre entre ces caractéristiques est nécessaire pour assurer le bon rendement de ces algorithmes. Un mécanisme exploratoire est présente dans cet article afin d’améliorer le rendement de l’algorithme d’optimisation par essaim de particules. La méthode est basée sur l’introduction, dans l’algorithme, d’une exploration artificielle en repositionnant aléatoirement les particules s’approchant de l’état stationnaire. Une mesure de la vitesse sert à distinguer entre les particules volantes et stationnaires. Cela peut se présenter comme une mort subite suivie d’une re-naissance de ces particules. Deux options de mécanisme de re-naissance sont vérifiées : (i) effacer et (ii) conserver la mémoire des particules en re-naissance. La meilleure particule globale est exemptée du processus de re-naissance afin que les parties les plus utiles des expériences passées de l’essaim ne soient pas perdues. La méthode est utilisée dans un problème d’évaluation de la conception de réseaux d’eaux pluviales; les résultats sont présentés et comparés à ceux de l’algorithme original et à ceux obtenus par d’autres méthodes. La méthode proposée, bien que simple, s’est avérée très efficace pour éviter l’optimum local, permettant ainsi d’améliorer l’algorithme sans demande computationnel additionnelle. [ABSTRACT FROM AUTHOR]