Pipe Routing through Ant Colony Optimization

As the need to better manage scarce water resources and water distribution systems increases, the problem of efficient routing of piping networks is gaining importance within the framework of an overall strategy for improving the networks' efficiency and resilience to undesired emphoperational changes. The paper presents a methodology for optimizing flow routing in pipe networks by imitating the natural selection processes used by real-life ants in search of the shortest path to a food source. The method, known as ant colony optimization (ACO), is a population-based, artificial multiagent, general-search technique for the solution of combinatorial problems with its analogical roots based on the behavior of real-ant colonies. ACO's mathematical background is outlined and a suggested possible implementation strategy is described for identifying shortest paths in water pipe networks. Such shortest paths could be not only the minimum pipe lengths between nodes of interest, but also the minimum number of valve operations required to keep a flow path active, the minimum number of customers affected during a flow reroute either because of planned (maintenance) or unplanned (water leak) conditions, and the minimum pressure drop along a path during adverse conditions. The ACO methodology should be of interest to both researchers and practitioners since it provides an alternative method to routing optimizations, with a wide range of applications. A case study of a specific urban water distribution network is also described for the proposed ACO virtual multiagent approach.