Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm

Abstract

The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider that the impact is different when a leak occurs in different nodes. In this study, sensitivity functions of different nodes were obtained according to the influence of the leakage of each node on the water distribution network. Combined with the water pressure correlation and water pressure sensitivity between nodes, the monitoring range of monitoring points and the water demand of covering nodes of monitoring points were taken as objective functions to build an optimal layout model. Taking a pipeline network in Qingdao as an example, the model was solved by using multi-objective White Whale Optimization and NSGA-II. By comparing the operation results of the four cases, it was found that the monitoring points found using multi-objective White Whale Optimization show better searching ability in terms of the sensitivity functions of different nodes.

Research on WSN coverage optimization of improved dung beetle algorithm in 3D environment

“…Finally, the sparrow search algorithm was adopted to optimize the local development stage and enhance the local development ability of the original algorithm. The WSN coverage optimization problem was compared with the outstanding swarm intelligence algorithms SSA [6] , BWO [7] , WOA [8] , NGO [9] and DBO [10] in recent years. YDBO has excellent stability and convergence in covering optimization problems and test sets, and its performance is better than other algorithms.…”