A multi-objective optimization prediction approach for water resources based on swarm intelligence
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RESEARCH PAPER
A multi-objective optimization prediction approach for water resources based on swarm intelligence Feng Zhang 1 & Yongheng Zhang 1 Received: 12 June 2020 / Accepted: 31 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The purpose of this study was to investigate improve the utilization rate of water resources and using multi-objective optimization algorithms to prediction water demand for the next 30 years, then the swarm intelligence approach was used to analysis for the development and utilization of water resources. The results obtained in this study include built a group optimization intelligent algorithm and the multi-objective optimization configuration prediction model for water resources is realized, for the disadvantages of PSO and GA algorithm, a hybrid optimization algorithm based on PSO and GA is proposed. The results indicated that the proposed algorithm can predict the water demand in the next 30 years, and can provide a certain reference for the formulation to effective regulation economic, social and ecological water consumption. Simultaneously, the PSO and GA hybrid optimization algorithm can achieve more than a simple algorithm using PSO or GA optimization results better. Keywords Hybrid optimization algorithm . Prediction model . Water resources . Data analysis . Multi-objective optimization
Introduction Water is an important resource for human survival, water resource management and decision-making is a research hotspot at home and abroad, and also a key scientific problem in the field of resource management. However, due to the severe damage to the ecological environment, the protection and rational use of water resources has become a concern of contemporary society (Alizadeh and Mousavi 2013; Ashraf et al. 2015). With the global climate change, the population has increased dramatically, the water crisis has intensified, the ecological environment has been seriously damaged, and the research on optimal allocation of water resources has become a hot issue at home and abroad. Especially after the twentieth century, water resources allocation issues have attracted the attention of relevant organizations around the world (Li et al. 2013; Zhang 2019). In cited as reference (Zhang 2019), we study how to collect water resources data through sensors in Communicated by: H. Babaie * Feng Zhang [email protected] 1
School of Information Engineering, Yulin University, Yulin 719000, Shaanxi, China
the Internet of things, then the original data collected is compressed by multi-objective optimization algorithm, in order to reduce the amount of water resources data. This paper is based on the research results of the previous article cited as reference (Zhang 2019), coupled with social, economic and environmental data, multi-objective optimization algorithm is used to analyze multi-source and heterogeneous water resources, in order to better regulate and control water resources in the decision support system. On the one hand, social and economic developm
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