Ant Colony Optimization for Upgrading and Expansion of Water Distribution System

Water supply network plays an important role in the protection of economic construction and people 's living . With the continued economic development of the continuous expansion of the city size , urban development facing the water distribution network expansion . Investment in water supply network , high cost , reasonable pipe network optimization renovation and expansion to be able to save a lot of investment , to achieve good economic and social benefits . In this paper, the renovation and expansion of a city 's water supply network works . More accurately investigate the pipe network analysis model is the necessary basis for pipe network renovation and expansion optimization . In this paper, the actual project , the establishment of a microscopic model of the city pipe network , and the model calibration and Assessment optimal design for the renovation and expansion of the pipe network system has laid a solid foundation . Single- condition pipe network design for maximum traffic diameters larger value , the renovation and expansion of water supply network based on multiple loading conditions considered optimal design model . Complex nonlinear programming problem , for the optimal design of the renovation and expansion of its decision-making variables for discrete variables and their objective function for the characteristics of multi- peak function , this paper, a district pipe network optimization studies , respectively, using a genetic algorithm , ant colony algorithm and Ant colony optimization algorithms for computational analysis . The results show that : Ant colony optimization algorithms global optimal solution easier to implement , and optimize efficiency higher . Ant Colony a city water supply network renovation and expansion of the actual engineering applications show that the improved algorithm with the traditional design method : optimal design than the traditional design to save the cost of the pipe network model of multi- condition model is superior to the single condition .