EMPSO showed promising results, when compared with other techniques. The proposed approach is first tested on a hypothetical multireservoir system, used by earlier researchers. In this study the standard particle swarm optimization (PSO) algorithm is further improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called particles. This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm optimization (EMPSO) technique, to derive reservoir operation policies for multipurpose reservoir systems. The proposed algorithm is applied to example reservoir systems used for water supply and hydropower. The process of improved policy generation and evaluation is repeated until no further improvement in performance is obtained. The better performing policies are then used as a basis for generating new sets of possible policies. Each policy is then evaluated using simulation to compute a performance index for a given flow series. Elitism, arithmetic crossover, mutation, and “en bloc” replacement are used in the algorithms to generate successive sets of possible operating policies. The genetic algorithms use real-valued vectors containing information needed to define both system release and individual reservoir storage volume targets as functions of total storage in each of multiple within-year periods. This paper focuses on the use of genetic search algorithms to derive these multireservoir operating policies. Multireservoir operating policies are usually defined by rules that specify either individual reservoir desired (target) storage volumes or desired (target) releases based on the time of year and the existing total storage volume in all reservoirs. Then, the algorithm is adopted for optimal operation of a four-reservoir system located in Karkheh river basin to properly meet agricultural requirements and to decrease the probability of major failures and finally, the results are provided. The research initially evaluates capability of extended algorithm using eight benchmark problems comparing other well-known metaheuristic algorithms, and verifies its effectiveness. The self-adaptive mechanism is implemented to satisfy problems constraints and perform algorithm parameters evolution going through different iterations. In the present study a Self-adaptive version of Melody Search algorithm is presented and applied to obtain Operating Rule Curves for multi-reservoir systems. The problems, because of broad search space, nonlinear relationships, correlation of several variables, as well as problem uncertainty, are difficult requiring powerful algorithms with specific capabilities to be solved. Operation of multi-reservoir systems is known as complicated and often large-scale optimization problems.

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