Optimization of Water Allocation During Water Scarcity Condition Using Non-Linear Programming, Genetic Algorithm and Particle Swarm Optimization (Case Study)

Water resources limitations, increasing water demands, and occurrence of frequent droughts in our country call for saving water programs and efficient use of available water supply. In this regard the optimization approaches can be an efficient tool. The main objective of this study is the comparison of three optimization approaches including Non-Linear Programming (NLP), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) when applied to the water allocation management during droughts. These are used to maximize the income in Zayandeh rud irrigation system in Esfahan, Iran during the 1999 -2001 drought. Each model considered the four layers; Chadagan dam operation, irrigation networks, crops, and growth stages. Comparison of the results showed that the highest income is obtained by NLP. Furthermore, this optimization method can increase the irrigation network income by about a 36% compared to the traditional managements.