جستجوی مقالات مرتبط با کلیدواژه « heuristic methods » در نشریات گروه « کشاورزی »

In the management water resources science, optimal Operation of existing water systems, such as dams, every day is more important. Due to budget and operational water resources limitations and environmental problems, optimize operation of these systems are gradually replaced by new systems are constructed. Optimal Operation of water resources is a Complex, nonlinear, multi-constraint and multidimensional optimization Problem, so to solve them robust optimization techniques needs are needed. In this study Genetic Algorithm optimization has been used in operation of the Mahabad reservoir Dam in Northwest of Iran. The objective function is minimization of difference between downstream monthly demand and release. Early; Sensitivity analysis of GA model performed by considering of various parameters. Then the method was applied considering the reduce probability of inflow to dam for period 24months in the different scenarios. The results show that, the critical condition of drought could managed by the GA model, the optimized model could satisfy downstream watery demand.

The Mont Carlo simulation is widely used for data generation based on specified distribution. However, generation of correlated data is only possible for specific distributions (bivariate normal), while it is required to deal with non-normal distributions, e.g. in regional frequency analysis. To tackle the problem, an innovative approach was proposed by using the genetic algorithm. Since it is possible only to change the order of numbers in an array, our problem is not completely in agreement with the classic genetic algorithm. Therefore, we reordered the algorithm; the internal of each chromosome was changed instead of crossing two different ones. The proposed method was applied for one random variable, and it was shown that the genetic algorithm produces multi-solutions, while unique solution is obtained using the Mont Carlo simulation procedure. We presented an appropriate objective function to include any bivariate distribution and showed its hither versatility as compared with the Mont Carlo simulation (only for the bivariate normal distribution). The multi-solutions in our proposed methodology is due to the fact that any array can be reordered in different possible combinations, each of which having a unique correlation coefficient with another fixed array. It was shown that these different combinations increase as the correlation coefficient decreases.