Global Optimization of Deficit Irrigation Systems Using Evolutionary Algorithms

Water is a limited resource and the dramatically increasing world population requires a significant increase in food production. For improving both crop yield and water use efficiency, the usual optimization strategy in irrigation at the field level considers scheduling parameters, i.e. when and how much to irrigate, as well as control parameters, i.e. the intensity and the irrigation time, for each water application. Optimizing control and schedule parameters in irrigation is considered as a nested problem. The objective of the global optimization is to achieve maximum crop yield with a given, but limited water volume, which can be arbitrary distributed over the number of irrigations. It is difficult to solve the global optimization problem, because the target function has many locally optimal solutions and the number of optimization variables, i.e. the number of irrigations is unknown a-priori. For this reason, a made to measure evolutionary optimization technique (EA) is employed to find a near-optimal solution of the global optimization problem within acceptable computational time. The results provided by the new optimization strategy are compared with the popular shuffled complex evolution algorithm (SCE-UA) optimization algorithm, simulated annealing (SA) and differential evolution (DE). The comparison demonstrated a striking superiority of the new tool with respect to both the achieved irrigation efficiency and the required computational time.