Study on optimal operation of reservoir based on directional self-learning genetic algorithm

A directional self-learning genetic algorithm is proposed in this paper, and it is applied to the optimal operation of reservoir. A directional self-learning system is introduced to improve the traditional generic algorithm that is typically slow in convergence and subject to premature convergence. In the new algorithm, directional information or pseudo-gradient of the function is introduced to guide the local searching. In addition a withering operator is proposed to increase population diversity, thus premature convergence and dimensional disaster risk are eliminated, and greater convergence speed is achieved. A case study indicates that, in contrast with the traditional generic algorithm, the directional self-learning generic algorithm shows advantages in computing cost and convergence for the optimization of reservoir operation.