基于改进后局部搜索算法的猕猴桃预估产量在种植区域的分配性能
Allocation performance of kiwifruit estimated yield in planting areas based on improved local search algorithm
黄珍 1景月楼2
作者信息
- 1. 西安明德理工学院经济与管理学院,西安 710124
- 2. 西安理工大学经济与管理学院,西安 710048
- 折叠
摘要
为进一步提高猕猴桃(Actinidia chinensis Planch.)预估产量在种植区域的分配性能,通过麻雀搜索算法、可变螺旋因子来改进局部搜索算法,并利用逐维透镜学习策略加快改进后局部搜索算法的收敛速度.结果表明,当猕猴桃预估产量不变时,随着可变螺旋因子数值增加,分配时间逐渐降低;当可变螺旋因子数值不变时,猕猴桃预估产量增加,分配时间也增加.猕猴桃预估产量分别为10、20、30、40、50、60 t,4个种植区域面积分别为500、650、700、850 m2.建议当猕猴桃预估产量为10～60 t时,可变螺旋因子数值设定为4;当预估产量为10～40 t时,种植区域3可以满足最佳种植间隔,当预估产量为50～60 t时,种植区域4可以满足最佳种植间隔,根据不同预估产量及种植区域面积合理进行猕猴桃种植间隔分配,保证猕猴桃获得充足的养分.改进后局部搜索算法的收敛速度较快,在迭代500次时,算法已趋于收敛,深度学习、粒子群算法及灰狼算法的收敛速度均小于改进后局部搜索算法.
Abstract
In order to further improve the allocation performance of kiwifruit(Actinidia chinensis Planch.)estimated yield in planting areas,local search algorithms were improved through sparrow search algorithm and variable helix factor,and the learning strategy of progressive lenses was used to accelerate the convergence speed of the improved local search algorithm.The results showed that when the estimated yield of kiwifruit remained unchanged,the allocation time gradually decreased as the value of the variable helix factor in-creased;when the value of the variable helix factor remained unchanged,the estimated yield of kiwifruit increased and the allocation time also increased.The estimated yields of kiwifruit were 10,20,30,40,50,and 60 tons respectively,with four planting areas of 500,650,700,and 850 m2.It was recommended to set the variable helix factor value to 4 when the estimated yield of kiwifruit was 10～60 tons;when the estimated yield was 10～40 tons,planting area 3 could meet the optimal planting interval.When the estimated yield was 50～60 tons,planting area 4 could meet the optimal planting interval.Kiwifruit planting interval was reasonably allocated based on different estimated yields and planting areas to ensure that kiwifruit received sufficient nutrients.The improved local search algorithm had a faster convergence speed,and by 500 iterations,the algorithm had tended to converge.The convergence speed of deep learning,particle swarm optimization algorithm,and grey wolf algorithm was lower than that of the improved local search algorithm.
关键词
改进后局部搜索算法/猕猴桃(Actinidia/chinensis/Planch.)/预估产量/种植区域/种植间隔/可变螺旋因子/分配性能Key words
improved local search algorithm/kiwifruit(Actinidia chinensis Planch.)/estimated yield/planting areas/planting inter-val/variable helix factor/allocation performance引用本文复制引用
基金项目
西安明德理工学院科研基金资助项目(2023MDY08)
陕西省自然科学基础研究计划项目(2023-JC-QN-0780)
陕西省教育厅科研计划项目(21JK0315)
出版年
2024
NETL
NSTL
万方数据