Simultaneous layout and pipe size optimization of pressurized irrigation networks

The layout of irrigation water distribution networks is usually branched. In most of the previous studies pipe sizes of the network had been optimized assuming a predetermined layout of the distribution system. However, only few researches have focused on the simultaneous layout and pipe size optimization. In this study a hybrid approach is adapted for simultaneous layout and pipe size optimization of branched pipe networks as a cost minimization problem. This new approach is based on combination of a pipe size optimizer (LIDM) with a layout optimizer for joint layout and pipe size optimization. At each iteration, the layout optimizer algorithm acts as an outer loop and LIDM acts as an inner loop. Once all the solutions are developed (each solution is a specific layout), LIDM can be used to optimize pipe sizes of each developed branched layout (solution). Then solution costs can be calculated and according to them, layout optimizer rearranges the solutions and the process continues. Two different approaches are used for layout optimization. At the First approach by using the loop model each branched layout is encoded as a string of eliminating links and a Discrete Particle Swarm Optimization for combinatorial optimization, called JPSO is applied to select the best sting of eliminating links. To avoid the problems associated with the Loop Model, at the second approach instead of coding the solutions into strings, an especial form of Genetic Algorithm which includes a tree growing algorithm within its “reproduction” phase is used as the layout optimizer. Proposed methods are applied for simultaneous layout and pipe size optimization of a small benchmark example in the literature and the results are presented and compared to the existing results. The results showed that the developed methods have significant advantages compared to other methods used. Afterwards, another example is considered to illustrate the efficiency of the proposed Genetic Algorithm for layout and size optimization of real-world networks.