Optimization of Water Resources Utilization by PSO-GA

• Published in: Water resources management Vol. 27; no. 10; pp. 3525 - 3540
• Main Authors: Chang, Jian-xia, Bai, Tao, Huang, Qiang, Yang, Da-wen
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2013; Springer; Springer Nature B.V
• Subjects: Adjustable; Algorithms; Atmospheric Sciences; China; Civil Engineering; Climate change; Driving ability; Dynamic programming; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Environment; Exact sciences and technology; Freshwater; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hydroelectric power; Hydrogeology; Hydrology; Hydrology. Hydrogeology; Hydrology/Water Resources; irrigation; Irrigation water; Linear programming; Methods; model validation; natural selection; Optimization; Random variables; Reservoirs; River basins; river water; Rivers; Runoff; Shortages; Studies; Utilization; Water demand; Water diversion; Water resources; Water resources management; Water shortages; Water supply; water utilization; watersheds; Far East; Asia; China; Multi-objective optimization model; PSO-GA; Reservoir operation; Water resources; rivers; algorithms; models; projects; reservoirs; surface water; irrigation; water resources; optimization; Multi-objective otimization model; water resource management; particles
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-013-0362-8

The objective of this paper is to present an optimal model to address the water resources utilization of the Tao River basin in China. The Tao River water diversion project has been proposed to alleviate the problem of water shortages in Gansu Province in China. A multi reservoir system is under consideration with multiple objectives including water diversion, ecological water demand, irrigation, hydropower generation, industrial requirements, and domestic uses in the Tao River basin. A multi-objective model for the minimization of water shortages and the maximization of hydro-power production is proposed to manage the utilization of Tao River water resources. An adjustable PSO-GA (particle swarm optimization – genetic algorithm) hybrid algorithm is proposed that combines the strengths of PSO and GA to balance natural selection and good knowledge sharing to enable a robust and efficient search of the solution space. Two driving parameters are used in the adjustable hybrid model to optimize the performance of the PSO-GA hybrid algorithm by assigning a preference to either PSO or GA. The results show that the proposed hybrid algorithm can simultaneously obtain a promising solution and speed up the convergence.