Nowadays, the scarcity of freshwater sources, climate change and the deterioration of freshwater quality have a great impact on the lives of human beings. As such, improving the design of irrigation canals will reduce water losses through evaporation and seepage. In this paper, particle swarm optimization (PSO) is used to determine the optimum design of irrigation canals' cross-sections with the objective to minimize the overall costs. The overall costs include the costs of earthwork, lining, and water loss by both seepage and evaporation. The velocity constraints for sedimentation and erosion have been taken into consideration in the proposed design method. The proposed PSO is compared with both the Probabilistic Global Search Lausanne (PGSL) and classical optimization methods to verify its usefulness in optimal design of canals' cross-sections. The proposed PSO is then used to design El-Sheikh Gaber canal, north Sinai Peninsula, Egypt and the obtained dimensions are compared with the existing canal dimensions. To facilitate the use of the developed model, optimal design graphs are presented. The results show that the reduction of overall cost ranged from 28 to 41% and consequently, the proposed PSO algorithm can be reliably used for the design of irrigation open canals without going through the conventional and cumbersome trial and error methods.

如今，淡水资源的匮乏，气候变化和淡水水质的恶化对人类的生活产生了巨大影响。因此，改进灌溉渠的设计将减少因蒸发和渗漏而造成的水损失。在本文中，粒子群优化（PSO）用于确定灌溉渠横截面的最佳设计，目的是最大程度地降低总体成本。总成本包括土方，衬砌以及因渗流和蒸发造成的水损失的成本。在提出的设计方法中已经考虑了沉积和侵蚀的速度约束。将拟议的PSO与洛桑概率全球搜索（PGSL）和经典优化方法进行了比较，以验证其在渠道优化设计中的有用性。交叉区域。然后将拟议的PSO用于设计埃及北部西奈半岛北部的El-Sheikh Gaber运河，并将获得的尺寸与现有运河尺寸进行比较。为了便于使用已开发的模型，提供了最佳设计图。结果表明，总体成本降低了28％到41％，因此，所提出的PSO算法可以可靠地用于灌溉渠的设计，而无需经历常规且繁琐的试验和错误方法。