OPTIMIZATION OF URBAN WATER SUPPLY NETWORK BASED ON DYNAMIC PRUNING MODEL

The optimal scheduling model for urban water supply networks often requires repeated scheduling of the hydraulics model to calculate the objective function and constraints,which leads to high computational and time cost.In the optimization iteration process,searching is usually performed at the boundaries of the constraints in the search space to obtain better optimization results,which leads to a large number of samples that do not satisfy the constraints and optimization efficiency.To solve this problem,an efficient constraint-based dynamic pruning method was proposed,which used the data accumulated during the optimization calculation to determine whether the samples met the constraints before running the hydraulics model,to eliminate the samples that do not satisfy the constraints and improve the optimization efficiency.The model was tested on a case network and a real network,and the results showed that the use of Naïve Bayes,decision trees,and support vector machines as dynamic pruning models reduced the number of computation by 56.4%,58.5%,and 56.8%,respectively,while obtaining the similar results as the original optimization model.