Using a hybrid genetic algorithm-simulated annealing algorithm for fuzzy programming of reservoir operation

We present a novel approach for optimizing reservoir operation through fuzzy programming and a hybrid evolution algorithm, i.e. genetic algorithm (GA) with simulated annealing (SA). In the analysis, objectives and constraints of reservoir operation are transformed by fuzzy progamming for searching the optimal degree of satisfaction. In the hybrid search procedure, the GA provides a global search and the SA algorithm provides local search. This approach was investigated to search the optimizing operation scheme of Shihmen Reservoir in Taiwan. Monthly inflow data for three years reflecting different hydrological conditions and a consecutive 10-year period were used. Comparisons were made with the existing M-5 reservoir operation rules. The results demonstrate that: (1) fuzzy programming could effectively formulate the reservoir operation scheme into degree of satisfaction a among the users and constraints; (2) the hybrid GA-SA performed much better than the current M-5 operating rules. Analysis also found the hybrid GA-SA conducts parallel analyses that increase the probability of finding an optimal solution while reducing computation time for reservoir operation. Copyright (C) 2007 John Wiley & Sons, Ltd.