STUDY OF CORN OPTIMIZATION IRRIGATION MODEL BY GENETIC ALGORITHMS

机译：遗传算法的玉米优化灌溉模型研究

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Many factors affecting irrigation model, including irrigation water volume, crop water requirement, production function of irrigation water, rainfall, soil water balance, water sensitive index in different stages of crop growth, the grain market price, irrigation water price, minimum yield, irrigation cost etc are considered. Then a multi-constraints and non-linear optimization irrigation model based on the maximal profit of irrigation water volume is set up, which is adaptive to our national conditions, and the real number encoding space of the model is searched by the powerful searching ability of genetic algorithm. The results show that this model can solve the optimization irrigation problem of summer corn, and genetic algorithm has very perfect searching function, and the optimal solution of the model can be found in very short time.

机译：影响灌溉模式的因素很多，包括灌溉水量，作物需水量，灌溉水的生产功能，降雨量，土壤水平衡，作物生长不同阶段的水敏感指数，粮食市场价格，灌溉水价格，最低产量，灌溉成本等。然后建立了一个基于灌溉水量最大收益的多约束非线性优化灌溉模型，该模型适应我国国情，并通过强大的搜索能力搜索模型的实数编码空间。遗传算法。结果表明，该模型可以解决夏玉米的优化灌溉问题，遗传算法具有很好的搜索功能，可以在很短的时间内找到最优解。

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来源
《IFIP(International Federation for Information Processing) TC 12 International Conference on Computer and Computing Technologies in Agriculture(CCTA 2007); 20070818-20; Wuyishan(CN)》|2007年|P.121132|共2页
会议地点 Wuyishan(CN)
作者
Bing Zhang; ShouQi Yuan; JianSheng Zhang; Hong Li;
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作者单位

Changzhou institute of technology, Chang Zhou, China;

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原文格式 PDF
正文语种 eng
中图分类电子技术、计算机技术在农业上的应用;
关键词
genetic algorithms; real number encoding; Jensen model; objective function; optimization irrigation;

机译：遗传算法实数编码詹森模型目标函数优化灌溉;

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专利

1. A combined simulation-genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project, Thailand [J] . Watchara Suiadee, Tawatchai Tingsanchali Hydrological Processes . 2007,第23期

机译：水库优化规则曲线的模拟-遗传算法组合优化模型：以泰国南翁灌溉项目为例
2. Phenomenological model-based study on electron beam welding process, and input-output modeling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm [J] . Das Debasish, Pratihar Dilip Kumar, Roy Gour Gopal, Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies . 2018,第9期

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3. Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture. [J] . Ines A V M, Honda K, Gupta A D, Agricultural Water Management . 2006,第3期

机译：将遥感模拟模型与遗传算法优化相结合，探索灌溉农业中的水资源管理方案。
4. Study of Corn Optimization Irrigation Model by Genetic Algorithms [C] . Bing Zhang, ShouQi Yuan, JianSheng Zhang, IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture . 2008

机译：遗传算法研究玉米优化灌溉模型
5. Optimization of seasonal irrigation scheduling by genetic algorithms. [D] . Canpolat, Necati. 1997

机译：利用遗传算法优化季节性灌溉调度。
6. Designing an intelligent monitoring system for corn seeding by machine vision and Genetic Algorithm-optimized Back Propagation algorithm under precision positioning [O] . Jiangtao Ji, Yayuan Sang, Zhitao He, 2021

机译：精密定位机器视觉和遗传算法优化后传播算法设计玉米播种智能监测系统
7. Study of Corn Optimization Irrigation Model by Genetic Algorithms [O] . Bing Zhang, ShouQi Yuan, JianSheng Zhang, -1

机译：遗传算法研究玉米优化灌溉模型

STUDY OF CORN OPTIMIZATION IRRIGATION MODEL BY GENETIC ALGORITHMS

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