Synchronization Optimization of Pipeline Layout and Pipe Diameter Selection in a Drip Irrigation Network System Based on the Jaya Algorithm

• Published in: Water (Basel) Vol. 16; no. 20; p. 2913
• Main Authors: Fan, Kai, Zhao, Tiantian, Yu, Xingjiao, Wang, Wene, Hu, Xiaotao, Ran, Danjie, Huo, Xuefei, Wang, Yafei, Pi, Yingying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Algorithms; Comparative analysis; Computers; Construction costs; Design; Design and construction; Dynamic programming; Efficiency; Fixed assets; Genetic algorithms; Irrigation; Linear programming; Management; Mathematical models; Mathematical optimization; Network management systems; Optimization techniques; Pipes; Technology application; Trickle irrigation; Water resources; Water-pipes; China
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w16202913

To address the complexity and high computational burden in the design of drip irrigation networks, the Jaya algorithm is utilized to study factors affecting project costs, including equipment and pipeline depreciation and the operation and management costs of the irrigation area. A mathematical model of synchronization optimal design of pipe layout and pipe diameter selection in a drip irrigation network system with constraints on pipe diameter, flow velocity, and pipe pressure is established. Using an irrigation district in Xinjiang, China, as an example, the Jaya algorithm optimization design program was run independently 50 times, and the relative deviation of each optimization result from the optimal solution was calculated. The results show that the annual cost per unit area o is reduced to 635.99 RMB/hm2, a 25.34% reduction compared to the original engineering program, and the investment-saving effect is obvious. The relative deviation is controlled within 3%, which shows that the algorithm has stable convergence performance and can meet the requirements of actual engineering design. The Jaya algorithm eliminates the need for parameter tuning, and it excels in cost savings, algorithm stability, and computational accuracy, making it an effective method for the single-objective optimization design of drip irrigation networks.