Abstract
Studies have shown that the performance of multi-objective evolutionary algorithms depends to a large extent on the shape of the Pareto fronts of the problem. Although, most existing algorithms have poor applicability in dealing with this problem, especially in the multi-objective optimization operation of reservoirs with unknown Pareto fronts. Therefore, this paper introduces an evolutionary algorithm with strong versatility and robustness named the Multi-Objective Evolutionary Algorithm with Reference Point Adaptation (AR-MOEA). In this paper, we take two water conservancy hubs (Huangjinxia and Sanhekou) of the Hanjiang to Wei River Water Diversion Project as example, and build a multi-objective operation model including water supply, ecology, and power generation. We use the AR-MOEA, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and the Indicator-Based Evolutionary Algorithm (IBEA) to search the optimal solutions, respectively. We analyze the performance of four algorithms and the operation rules in continuous dry years. The results indicate that (1) the AR-MOEA can overcome the difficulty of the shape and distribution of the unknown Pareto fronts in the multi-objective model. (2) AR-MOEA can improve the convergence and uniformity of the Pareto solution. (3) If we make full use of the regulation ability of the Sanhekou reservoir in the dry season, the water supply for coping with possible continuous dry years can be guaranteed. The study results contribute to the identification of the relationship among objectives, and is valued for water resources management of the Hanjiang to Wei River Water Diversion Project.
Highlights
Water resources play an increasingly indispensable role in regional development with the development of social economy and the acceleration of urbanization [1]
The results indicate that (1) the AR-Multi-Objective Evolutionary Algorithm (MOEA) can overcome the difficulty of the shape and distribution of the unknown Pareto fronts in the multi-objective model
A multi-objective model that considered water supply, ecology, and power generation was established and solved by four evolutionary algorithms, the performance of AR-MOEA was verified by using the Hanjiang to Wei River Water Diversion Project datasets
Summary
Water resources play an increasingly indispensable role in regional development with the development of social economy and the acceleration of urbanization [1]. Climate change and human activities have intensified the spatial and temporal distribution of water resources in some regions [2,3], which further causes resource-based and water-based water shortages. As an important engineering measure to change the spatial and temporal distribution of water resources, the inter-basin water transfer project can effectively alleviate the contradiction between water supply and demand and improve the ecological environment in the water receiving area [4,5], such as the. The inter-basin water transfer project usually includes reservoir-pump-power station groups, water-storage user groups, and long-distance water transport facilities [7]. As a bridge connecting two or even multiple basins, multi-objective optimization operation of inter-basin water transfer projects has become one of the hot research topics in the current reservoir operation field
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