基于进化算法的高寒草地放牧牦牛种群优化问题研究

发布时间：2018-03-11 20:40

本文选题：牦牛　切入点：高寒牧场　出处：《青海大学》2017年硕士论文　论文类型：学位论文

【摘要】：遗传算法发展至今日,已成为了计算智能领域的一个重要分支。云模型是一种定性定量不确定性转换模型,它结合了模糊数学及概率论。云模型能够准确表达出自然语言的不确定性,可以为不确定性问题的研究及表达提供新的思路。使用传统的遗传算法解决实际问题,具有极大的随机性以及盲目性,同时具有搜索能力差,搜索慢等不足。因此,本文将云模型的不确定性转换能力使用到遗传算法中并对其进行改进,以加快收敛、加强算法寻优能力。由于本文模型的特殊性,本文主要利用正态云发生器代替变异运算,Y条件云发生器代替交叉运算,对种群进行更新。青海高寒草甸牧场管理技术比较原始及落后,畜群结构不合理、超载放牧现象严重,这导致生态环境破坏、牧民收益降低。为缓解这个问题,牧民需要采用科学的放牧模式。结合信息化技术及放牧系统优化理论,本文提出了一种求解高寒牧场牦牛种群结构优化问题的云遗传算法。考虑保护天然草场资源和畜牧业的可持续发展,以最大净货币价值收益和最大综合收益为目的,以牧草资源为约束条件,建立了高寒草地放牧种群结构优化模型,先用数学建模的方法对高寒牧场牦牛种群结构优化问题进行建模,然后通过改进的云遗传算法对模型进行仿真及其求解,最后分析实验结果,得到结论,其结果为高寒牧场生产计划提供了一定的指导作用。
[Abstract]:Genetic algorithm (GA) has become an important branch of computational intelligence field. Cloud model is a qualitative and quantitative uncertainty transformation model. It combines fuzzy mathematics with probability theory. The cloud model can accurately express the uncertainty of natural language, and can provide a new way of thinking for the research and expression of uncertainty problems. The traditional genetic algorithm is used to solve practical problems. Because of its great randomness and blindness, poor searching ability and slow searching ability, this paper applies the uncertainty transformation ability of cloud model to genetic algorithm and improves it to speed up convergence. Because of the particularity of this model, this paper mainly uses normal cloud generator instead of variation operation condition cloud generator to replace cross operation. The management technology of Qinghai alpine meadow pasture is primitive and backward, the structure of herd is unreasonable, and the phenomenon of overloading grazing is serious, which leads to the destruction of ecological environment and the decrease of herdsmen's income. In order to alleviate this problem, Herdsmen need to adopt scientific grazing mode, combining with information technology and grazing system optimization theory, In this paper, a cloud genetic algorithm is proposed to solve the problem of yak population structure optimization in alpine pastures. Considering the protection of natural grassland resources and the sustainable development of animal husbandry, the purpose of this algorithm is to maximize the net monetary value and the maximum comprehensive income. The optimization model of grazing population structure in alpine grassland was established with forage resources as the constraint condition. Firstly, the optimization of yak population structure in alpine pasture was modeled by mathematical modeling method. Then the model is simulated and solved by the improved cloud genetic algorithm. Finally, the experimental results are analyzed and the conclusion is drawn. The results provide some guidance for the production planning of alpine pasture.
【学位授予单位】：青海大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：S823.85;TP18

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