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Multi-objective Ecological Operation of Reservoir in Luanhe River Based on Improved Particle Swarm Optimization

机译:基于改进粒子群优化的滦河水库多目标生态运行

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摘要

River ecosystem is one of the most important ecosystems, and it provides many ecosystem services for human beings. However, river health has also been damaged by over-exploitation and water pollution. In the process of reservoir operation, the ecological flow demand of rivers should be fully considered and multi-objective ecological dispatch of reservoirs should be implemented. On basis of the traditional particle swarm optimization (PSO), the improved PSO with adaptive random inertia weights (ARIW) is proposed to deal with the problem of ecological optimal operation of reservoir in the paper. According to the evolutionary process, based on the probability distribution density function of triangle, the inertia weight can be adjusted randomly and adaptively to meet the global or local optimization requirements. By typical mathematical function, the improved PSO algorithm is compared with traditional PSO and genetic algorithm (GA), and is proved to be more efficient and accurate. Taking Panjiakou Reservoir on the main stream of Luanhe River in China as an example, the multi-objective ecological optimal dispatch of reservoir has been analysed and calculated with the improved PSO algorithm under different target years, considering flood control, water supply, and ecological demand. The research results can provide reference for developing rationally Luanhe River water resources, and making scientifically ecological dispatch plan of Panjiakou reservoir.
机译:河流生态系统是最重要的生态系统之一,它为人类提供了许多生态系统服务。然而,河流健康也受到过度剥削和水污染的损坏。在水库运作过程中,应充分考虑河流的生态流量需求,并应实施水库的多目标生态调度。在传统粒子群优化(PSO)的基础上,提出了具有适应性随机惯性重量(ARIW)的改进的PSO,以解决论文中水库生态最佳运行问题。根据进化过程,基于三角形的概率分布密度函数,可以随机调整惯性重量,以满足全球或局部优化要求。通过典型的数学函数,将改进的PSO算法与传统的PSO和遗传算法(GA)进行比较,并且被证明是更有效和准确的。在中国滦河河河口水库为例,是在不同目标年的改进的PSO算法,考虑防洪,供水和生态需求下分析并计算了水库的多目标生态最佳调度。 。研究成果可以参考发展理性滦河水资源,并制定潘家口水库科学生态派出计划。

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