基于GA-ACO混合算法的草地放牧环境规划研究

Shi Xiangnan, Yanbo Yang, Qiwei Xu, Teng Li, Jiawei Zhang
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引用次数: 0

摘要

针对牧民过度放牧和草地利用率下降的问题,提出了一种遗传算法-蚁群优化(GA-ACO)混合算法来解决牧区放牧问题。首先,对牧区环境进行分析,通过计算得到牧区的过牧区面积和可利用面积,并根据牲畜承载能力、羊数和放牧天数作为划分牧区的标准,设计遗传算法中的三种算子。连通性概念,从而保证算法合理划分牧区轮换区域。然后在此基础上,改进蚁群算法的信息回溯机制和动态检测机制,提高算法的收敛速度,快速找到草原牧区最短的放牧路径。最后,为了验证混合算法的有效性,采用不同规模的牧区随机生成地图进行算法仿真实验。实验结果表明,该算法能够合理划分旋转放牧区,有效避免过度放牧区,并快速规划最短放牧区路径,为无人机的实现提供了科学依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on grassland grazing environment planning based on GA-ACO hybrid algorithm
Aiming at the problems of overgrazing by pastoralists and the reduction of grassland utilization rate, a Genetic Algorithm-Ant Colony Optimization (GA-ACO) hybrid algorithm was proposed to solve the problem of grazing in pastoral areas. Firstly, the pastoral environment is analyzed, and the overgrazing area and the usable area of the pastoral area are obtained by calculation, and the three types of operators in the genetic algorithm are designed according to the stock carrying capacity, the number of sheep and the grazing days as the standard for dividing the grazing area. Connectivity concept, so as to ensure that the algorithm reasonably divides the pastoral rotation area. Then, on this basis, the information backtracking mechanism and dynamic detection mechanism of the ant colony algorithm are improved, so as to improve the convergence speed of the algorithm and quickly find the shortest path for grazing in grassland pastoral areas. Finally, in order to verify the effectiveness of the hybrid algorithm, random generation maps of different sizes of pastoral areas are used for algorithm simulation experiments. The experimental results show that the algorithm can reasonably divide the rotation grazing area, effectively avoid the overgrazing area, and quickly plan the shortest grazing path, which is the scientific basis for the unmanned aerial vehicle.
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