A high-dimensional multi-objective differential evolution algorithm based on global ranking is developed to identify the resistance coefficient of heat supply network, and the multi-objective algorithm is applied to the resistance identification of heat supply network, and the calculation process of resistance identification is improved. The fuzzy mathematics method is applied to the process of resistance identification, a set of optimal solution is generated by identifying each pipe segment, and the optimal solution is selected from the optimal solution set according to the fuzzy membership degree. The results show that compared with the single objective algorithm, the optimal solution set generated by the high-dimensional multi-objective differential evolution algorithm based on global ranking is uniformly distributed and concentrated, and the optimal solution obtained is more accurate.

With popularity of heating metering system, system can be adjusted according to the change of load, and the resistance coefficient of pipe network change. Optimal identification of variable resistance coefficient is an effective means to understand the real-time operation of heating network. We present an optimal identification method for variable resistance coefficient of heat supply network based on flow observation data. Genetic algorithm is used to solve the problem. Relative error of the identification results is less than 5% in practical verification example. It shows that the method can obtain variable resistance coefficient of heating network with high accuracy when only flow observation data is available. It can provide guidance for the operation and regulation of heating systems.