Research on Water Network Optimization Based on Adaptive Multi- Group Whale Optimization Algorithm

Due to the rapid development of society, the water supply infrastructure of many cities can no longer meet the needs of urban development, and it is necessary to expand, in order to solve the design problem of pipe diameter optimization, the pipe diameter optimization model is constructed, and an adaptive multi-group whale optimization algorithm is developed. The algorithm innovatively constructs a variety of improvement strategies, including a variety of group equilibrium strategies, the use of reinforcement learning model, adaptive optimization to adjust the key parameter values under each equilibrium strategy, and according to the flow rate and diameter value of the pipe segment, the next generation of individual pipe diameter selectable range adaptive adjustment, thereby improving the evolutionary efficiency. Finally, we conducted a practical application case test to verify the method using the New York tunnel network expansion problem as an example. The results show that the adaptive multi-group whale optimization algorithm has higher convergence speed and higher optimization efficiency than the traditional heuristic algorithm.