Optimal Allocation of Urban Water Resources Based on Multi-Objective Nutcracker Optimization Algorithm

Dong Wang

doi:10.3390/w16233475

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Optimal Allocation of Urban Water Resources Based on Multi-Objective Nutcracker Optimization Algorithm

2024-12-03

DOI：10.3390/w16233475

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Abstract

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The imbalance between water supply and demand (WSD) has been growing noticeable as a result of the economy's fast expansion which can be effectively alleviated using optimal allocation of water resources. An urban water resources allocation (WRA) model based on the innovative Multi-Objective Nutcracker Optimization Algorithm (MONOA) is proposed in this study. Taking into account economic, social and ecological benefits, a comprehensive multi-objective optimization (MOO) model is established. By introducing the opposite learning strategy, non-dominated sorting approach and crowding distance mechanism to a recently reported intelligent optimization algorithm called the Nutcracker Optimization Algorithm (NOA), the novel nature-inspired metaheuristic algorithm MONOA is proposed to solve the multi-objective optimization model. The MONOA is evaluated on ten benchmark test functions, and it exhibits superior distribution and convergence by comparing with some highly cited algorithms. The proposed model is applied to Handan, China, in order to obtain a reasonable water allocation scheme in the planning year. The simulation results reveal that the economic benefit is in the range CNY [1.36, 1.44] x 1011, water shortage is in the range [0.66, 0.98] x 108 m3 and COD emission is in the range [3.70, 3.91] x 104 t in all the obtained Pareto solutions. The water resources management departments might create customized water allocation plans by balancing different goals and taking preferences into account. Moreover, the proposed method is a general approach that can be applied to many other cities. Hence, it is of great significance to the sustainable development and utilization of urban water resources.

Keywords:

water resources allocation

multi-objective optimization

intelligent optimization algorithm

water supply

water demand