Application of MOHUS in Multi-Objective Optimal Allocation of Water Resources for the Central Route South-to-North Water Diversion Project in Hebei Province, China

With the increasingly severe problem of water shortage and the increasing contradiction between supply and demand, the optimal allocation of water resources has become an essential method for alleviating the water crisis. In this context, natural-element-inspired optimization algorithms have been extensively used to solve water resource optimization problems. The hunting search (HUS) algorithm has a slow convergence speed, and low accuracy, which makes it easy to fall into local optima when solving multi-objective problems, and it is also not easy to apply directly to multi-objective optimization. It is improved by introducing a fast, non-dominated sorting, congestion comparison operator, and elite retention strategy. The improved algorithm was evaluated against popular algorithms using the ZTD series of objective functions, and the results indicate that the improved algorithm performs better in terms of convergence and diversity of solution sets. The improved algorithm is then applied to solve the optimal allocation model for water resources in the receiving area of the South-to-North Water Diversion Project in Hebei Province, which is based on the objective of social and economic benefits. Based on the obtained Pareto optimal frontier, the scheme with a special preference for the minimum shortage of water resources is selected as the ultimate decision-making scheme. The optimization results show that in 2030, the total water demand of water users in the receiving area is 4661.82 × 10⁶ m³, the total water allocation is 4082.88 × 10⁶ m³, and the water deficit is 578.94 × 10⁶ m³. The improved hunting search algorithm offers a new solution method for the multi-objective water resource optimization allocation problem.