Computational modeling demonstrates that a sigmoid-adaptive genetic algorithm optimizes rotational canal scheduling in arid irrigation networks, indicating improved water-use efficiency.
Rotational canal scheduling in arid irrigation districts must satisfy coupled water-volume, timing, and hydraulic constraints while balancing modeled seepage loss and the dispersion of trunk-canal discharge levels. A constrained model with a weighted composite objective was solved using a sigmoid-adaptive genetic algorithm (SIGGA) integrating adaptive operators, feasibility-first comparison, and diversity restoration. The framework was evaluated in the Zhangye (Xijun) and Shihezi oasis irrigation districts, with two irrigation-quota scenarios in Shihezi. For Zhangye, φ = 0.5 gave the closest empirical compromise among five tested weights. Under an equal function-evaluation budget, SIGGA produced a median final composite-objective value 3.9% lower than that of the hybrid particle swarm optimization-simulated annealing genetic algorithm (GSPSO), the closest benchmark, and showed low run-to-run variability under fixed inputs. Removing feasibility-first comparison caused the largest increase in the final composite-objective value. Sensitivity analyses showed that outcomes varied with model inputs, temporal resolution, and adaptive-operator settings. The representative Zhangye schedule satisfied all modeled constraints and completed the modeled rotation in 10 d, compared with the reported 25-d reference schedule, with a canal water-use efficiency of 0.843. The same formulation and prespecified settings yielded feasible schedules for both Shihezi quota scenarios without quota-specific retuning. Across the original 60 jointly perturbed Zhangye scenarios, all 180 scenario-specific reoptimization runs were feasible, whereas the fixed nominal schedule remained feasible in 52 scenarios. An enlarged replay-only analysis yielded median fixed-schedule feasibility proportions of approximately 86.5% at the larger sample sizes within the prescribed bounded scenario space. The framework can support generation and updating of candidate rotational schedules within the examined oasis open-channel systems, although field validation and testing in more diverse canal networks remain necessary.
No takes yet. Share an insight, caveat, or question.
Yu et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: