Optimal irrigation allocation and reservoir operation are essential in combating water scarcity in arid and semi-arid regions like Iran. Due to the huge number of decision variables in a reservoir–farm system, the inseparable nature of the crop yield function and the large variety of constraints, a genetic algorithm (GA) and harmony search (HS) are employed in the current paper to construct an integrated reservoir–farm system (IRFS). This integrated model can take into account crop sensitivity to water stress in order to maximize the net benefit of crops and to optimize crop areas and irrigation scheduling. This methodology is applied to the multi-crop Aharchay irrigation system downstream of Sattarkhan Dam. The outputs include the optimal values of the cultivated area, irrigation depth, and water release from the reservoir in 10-day periods. The results showed the speed of convergence, the optimal value of the total benefit and the slightly greater yield values of the HS algorithm compared to those of the GA. Both models also resulted in higher benefit amounts compared to the real and attainable benefit in the region. Finally, regarding the speed of convergence to the maximum value of the objective function, the HS algorithm demonstrated more promising results than the GA in the current case of interest.

中文翻译：

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和谐搜索和遗传算法在综合储层-农场系统（IRFS）的同时优化中的应用*

最佳灌溉分配和水库运营对于解决伊朗等干旱和半干旱地区的缺水问题至关重要。由于水库-农场系统决策变量数量众多，作物产量函数不可分割，约束条件多样，​​本文采用遗传算法（GA）和和声搜索（HS）构建一个集成的水库-农场系统（IRFS）。这种集成模型可以考虑作物对水分胁迫的敏感性，以最大限度地提高作物的净收益并优化作物面积和灌溉计划。这种方法适用于 Sattarkhan 大坝下游的多作物 Aharchay 灌溉系统。输出包括耕地面积、灌溉深度和水库在 10 天内释放的最佳值。结果表明，与 GA 相比，HS 算法的收敛速度、总收益的最优值和屈服值略大。与该地区的实际和可实现的收益相比，这两种模型还产生了更高的收益金额。最后，关于收敛到目标函数最大值的速度，HS 算法在当前感兴趣的情况下表现出比 GA 更有希望的结果。