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A dynamic programming fuzzy rule-based (DPFRB) model for optimal operation of reservoirs system is presented in this paper. A deterministic Dynamic Programming (DP) model is used to develop the optimal set of inflows, storage volumes, and reservoir releases. These optimal values are then used as inputs to a Fuzzy Rule-Based (FRB) model to derive the general operating policies. Subsequently, the operating policies are evaluated in a simulation model while optimizing the parameters of the FRB model. The algorithm then gets back to the FRB model to establish the new set of operating rules using the optimized parameters. This iterative approach improves the value of the performance function of the simulation model and continues until the satisfaction of predetermined stopping criteria. The DPFRB performance is tested and compared to a model which uses the multiple regression based operating rules. Results show that the DPFRB performs well in terms of satisfying the system target performances.

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In this paper, a new Continuous Ant Colony Optimization (CACO) algorithm is proposed for optimal reservoir operation. The paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. The paper also proposes an elitist strategy for CACO algorithm where best solution of each iteration is directly copied to the next iteration to improve performance of the method. The performance of the CACO algorithm is demonstrated against some benchmark test functions and compared with some other popular heuristic algorithms. The results indicated good performance of the proposed method for global minimization of continuous test functions. The method was also used to find the optimal operation of the Dez reservoir in southern Iran, a problem in the reservoir operation discipline. A normalized squared deviation of the releases from the required demands is considered as the fitness function and the results are presented and compared with the solution obtained by Non Linear Programming (NLP) and Discrete Ant Colony Optimization (DACO) models. It is observed that the results obtained from CACO algorithm are superior to those obtained from NLP and DACO models.

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Through a collection of cooperative agents called ants, the near optimal solution to the multi-reservoir operation problem may be effectively achieved employing Ant Colony Optimization Algorithms (ACOAs). The problem is approached by considering a finite operating horizon, classifying the possible releases from the reservoir(s) into pre-determined intervals, and projecting the problem on a graph. By defining an optimality criterion, the combination of desirable releases from the reservoirs or operating policy is determined. To minimize the possibility of premature convergence to a local optimum, a combination of Pheromone Re-Initiation (PRI) and Partial Path Replacement (PPR) mechanisms are presented and their effects have been tested in a benchmark, nonlinear, and multimodal mathematical function. The finalized model is then applied to develop an optimum operating policy for a single reservoir and a benchmark four-reservoir operation problem. Integration of these mechanisms improves the final result, as well as initial and final rate of convergence. In the benchmark Ackley function minimization problem, after 410 iterations, PRI mechanism improved the final solution by 97 percent and the combination of PRI and PPR mechanisms reduced final result to global optimum. As expected in the single-reservoir problem, with a continuous search space, a nonlinear programming (NLP) approach performed better than ACOAs employing a discretized search space on the decision variable (reservoir release). As the complexity of the system increases, the definition of an appropriate heuristic function becomes more and more difficult this may provide wrong initial sight or vision to the ants. By assigning a minimum weight to the exploitation term in a transition rule, the best result is obtained. In a benchmark 4-reservoir problem, a very low standard deviation is achieved for 10 different runs and it is considered as an indication of low diversity of the results. In 2 out of 10 runs, the global optimal solution is obtained, where in the other 8 runs results are as close as 99.8 percent of the global solution. Results and execution time compare well with those of well developed genetic algorithms (GAs).