Adaptive Hybrid Genetic Algorithm and Cellular Automata Method for Reliability-Based Reservoir Operation

Abstract

An adaptive hybrid genetic algorithm (GA) and cellular automata (CA) method is proposed for solving implicit stochastic optimization of reservoir operation problems. The method is based on a decomposition approach in which the reliability constraints are handled with GA, whereas the resulting deterministic problem is solved with a CA model. Two versions, binary and integer GA, were employed for handling the reliability constraints of the problem. In the first one, GA was used to determine the success/failure pattern of the operation, whereas in the latter, only failure periods were determined with GA. The proposed method was used for monthly water supply and hydropower operation of an existing reservoir and the results are presented and compared with those of a GA model. To demonstrate the efficiency and scale independency of the model, short-term, medium-term, and long-term operations are considered assuming different target reliabilities. Comparison of the results with those of a GA model shows the superiority of the proposed method.