A multi-objective optimization design method for self-pressure drip irrigation networks considering economic efficiency and reliability

To address redundant investment costs, uneven hydraulic distribution, and insufficient operational reliability in conventional irrigation pipe‐network designs, this study establishes a multi-objective optimization model with the objectives of minimizing total investment cost (economy) and minimizing the mean and variance of nodal surplus heads (reliability). Guided by the characteristics of the solution space, we introduce three strategies—weighted intelligent initialization, adaptive variation, and soft‐constraint handling—and propose an enhanced Golden Jackal Optimization (GJO) algorithm tailored for random Rotational Irrigation Group (RIG) division. The superiority of the improved GJO in high‐dimensional, multi‐objective problems is validated through comparative simulations on the ZDT test‐function suite and against the NSGA‐II algorithm. A self‐pressurized drip irrigation project in Xinjiang serves as a case study: the optimized design reduces pipe‐network investment by 12.78 %, decreases the mean nodal surplus head by 11.45 %, and lowers the surplus‐head variance by 16.67 % compared with the original scheme, thereby demonstrating the method’s validity and practicality. Finally, correlation analysis elucidates the trade‐off relationships among pipe‐network investment cost, mean nodal surplus hydraulic head, and surplus‐head variance.