基于蜂群-粒子群算法的天然林空间结构优化

天然林空间结构包含林木的空间位置信息,影响着林木的生长、竞争、林分的稳定及森林的发展,其优化是个多目标规划问题。提出一种蜂群-粒子群(ABC-PSO)混合算法,该算法在初始粒子产生机制、随蜂数量及循环机制上对蜂群算法做了改进,并将其应用到天然林空间结构多目标优化中,最终建立能够兼顾林木分布格局、林木大小分割、林木竞争的优化模型。仿真实验表明,蜂群-粒子群算法提升了森林健康等级,解决了森林空间结构多目标优化问题。

Spatial Structure Optimization of Natural Forest Based on Bee Colony-particle Swarm Algorithm

The natural forest spatial structure contains the spatial location information of the forest, which affects the growth, competition and stability of the forest development. Its optimization is a multi-objective programming problem. A bee colony particle swarm optimization (ABC-PSO) hybrid algorithm is proposed, which improves the initial particle generation mechanism, the number of follow bees and the circulation mechanism. The algorithm is applied to the multi-objective optimization of the spatial structure of natural forest. An optimization model which takes account of the tree distribution grid, tree size segmentation and tree competition is established. The simulation results show that the bee colony-particle swarm optimization algorithm improves the forest health level and solves the multi-objective optimization of the forest spatial structure.