Abstract
Assessing the fairness of water resource allocation and structural water shortage risks is an urgent problem that needs to be solved for the optimal allocation of water resources. In this study, we established a new multi-objective optimization model of water resources based on structural water shortage risks and fairness. We propose an improved NSGA-III based on the reference point selection strategy (ARNSGA-III) to solve the optimization model. The superiority of this method was proven by comparing it with three other methods, namely, NSGA-III, MOSPO, and MOEA/D. The model was applied to optimize the allocation of water resources in Wusu City in China. The results show that the new multi-objective optimization model provides reasonable and feasible solutions for solving water conflicts. The convergence and stability of ARNSGA-III are better than those of the other three algorithms. Allocation schemes of water resources for Wusu City in normal years, dry years, and extremely dry years are proposed. In normal years, the structural water shortage risk index is reduced by 50.1%, economic benefits increased by 0.2%, and fairness is reduced by 60.5%. This study can provide new ideas for solving the multi-objective optimization of regional water resources.
Highlights
We propose an improved NSGA-III based on the reference point selection strategy (ARNSGA-III), in which information on differential distribution characteristics discriminates the evolution stage of the population, and reference points are selected based on the distribution characteristics of the population in the target space
Using ARNSGA-III, NSGA-III, MOSPO, and MOEA/D to solve the multi-objective optimization model of water resources established in this paper, the Pareto solution set calculated by each algorithm was put into the Hyper Volume (HV) index calculation formula
In order to analyze the impact of a single goal on other goals when it reaches its optimum potential in normal years, dry years, and extremely dry years, nine groups of different objective function values, with the smallest structural water shortage risk index f 1, the largest economic benefit f 2, and the largest fairness f 3, were selected for comparative analysis
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With the rapid development of computer technology, a large number of evolutionary algorithms have been proposed Because of their ability to perform large-scale and complex calculations and because they have the advantage of high versatility, they have been widely used to obtain solutions of water resource optimization models. Such methods include the genetic algorithm [14], particle swarm algorithm [15], non-dominated sorting genetic algorithm [16], and their modified versions [17]. A new multi-objective optimization model of regional water resources was established, and an improved NSGA-III (ARNSGA-III) method was used to solve the optimization model. This study can provide new ideas for the multi-objective optimization of regional water resources
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