Developing a Furrow Irrigation's Optimization Model Using Honey Bee Mating Algorithm

In many countries across the world، surface irrigation is one of the most important and most common ways to irrigate the fields. So paying attention to improving surface irrigation methods and increasing their efficiency looks necessary. Thus the best design approach is optimized design in which the considered efficiencies in their linear combination are optimized in one goal function. So the purpose of this study is using Sirmod Model، artificial neural network and honey bee mating optimization algorithm for simulation and optimization the surface irrigation parameters like furrow length، inlet discharge and cutoff time. The obtained results showed that the artificial neural network with high R- square for all the outputs has a good ability in simulating surface irrigation’s systems which is so much depended on the variety of the data bank and proper training of the network. Also linking neural network and honey bee mating algorithm in order to develop a simulationoptimization model provided acceptable results which can be used as optimal values in designing. The average values of optimizing design parameters are 202 meter for furrow length، 2. 13 liter per seconds for inlet discharge and 28. 6 minutes for cutoff time.