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Optimizing channel cross section in irrigation area using improved cat swarm optimization algorithm

机译:改进的猫群优化算法优化灌区河道断面

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This research aimed to design the channel cross section with low water loss in irrigation areas. The traditional methods and models are based on explicit equations which neglect seepage and evaporation losses with low accuracy. To rectify this problem, in this research, an improved cat swarm optimization (ICSO) was obtained by adding exponential inertia weight coefficient and mutation to enhance the efficiency of conventional cat swarm optimization (CSO). Finally, the Fifth main channel of Jiangdong Irrigation area in Heilongjiang Province was taken as a study area to test the ability of ICSO. Comparing to the original design, the reduction of water loss was 20% with low flow errors. Furthermore, the ICSO was compared with genetic algorithm (GA), the particle swarm optimization (PSO) and cat swarm algorithm (CSO) to verify the effectiveness in the channel section optimization. The results are satisfactory and the method can be used for reliable design of artificial open channels. Keywords: cat swarm optimization (COS), exponential inertia weight coefficient, adoptive mutation operation, water loss, cross section, open channel DOI: 10.3965/j.ijabe.20160905.2531 Citation: Liu D, Hu Y X, Fu Q, Imran K M, Cui S, Zhao Y M. Optimizing channel cross section in irrigation area using improved cat swarm optimization algorithm. Int J Agric & Biol Eng, 2016; 9(5): 76-82.
机译:本研究的目的是设计灌区低失水的河道断面。传统的方法和模型基于显式方程,该方程忽略了渗漏和蒸发损失,且精度较低。为了解决这个问题,在本研究中,通过添加指数惯性权重系数和变异来提高常规猫群优化(CSO)的效率,从而获得了改进的猫群优化(ICSO)。最后,以黑龙江省江东灌区第五主干道为研究区域,对ICSO的能力进行了测试。与原始设计相比,失水量减少了20%,流量误差低。此外,将ICSO与遗传算法(GA),粒子群优化(PSO)和猫群算法(CSO)进行了比较,以验证通道截面优化的有效性。结果令人满意,该方法可用于可靠的人工明渠设计。关键字:猫群优化(COS),指数惯性权重系数,过继突变操作,失水,横截面,明渠DOI:10.3965 / j.ijabe.20160905.2531引文:刘丹,胡艳霞,付琦,伊姆兰·KM,崔S,Zhao Y M.使用改进的猫群优化算法优化灌溉区的河道断面。国际农业与生物工程杂志,2016; 9(5):76-82。

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