• 论文与研究报告 •

### 基于遗传算法的森林抚育间伐小班智能选择

1. 北京林业大学信息学院 北京 100083
• 收稿日期:2016-04-01 修回日期:2016-06-28 出版日期:2017-09-25 发布日期:2017-10-24
• 基金资助:
十三五国家重点研发计划课题（2017YFD0600906）；"十二五"国家高技术研究发展计划（863计划）课题（2012AA102003）。

### Forest Thinning Subcompartment Intelligent Selection Based on Genetic Algorithm

Wang Jianming, Wu Baoguo, Liang Qiyang

1. School of Information Science and Technology, Beijing Forestry University Beijing 100083
• Received:2016-04-01 Revised:2016-06-28 Online:2017-09-25 Published:2017-10-24

Abstract: [Objective] This study investigated the intelligent selection method of subcompartments based on spatial analysis and genetic algorithm(SGA)in order to provide decision support for formulating forest management plan, conducting under the thinning target control.[Method] Huamugou forest farm, in Chifeng City, Inner Mongolia, was selected as research area to simulate intelligent selection. According to the basic condition of thinning target and operator, the initial small class collection was chosen from continuous distribution of tiny space by spatial query or point buffer analysis. Initial radius and step of point buffer analysis were calculated dynamically by annulus control algorithm(ACA). Urgency indicator, difficulty indicator and site indicator constituted the objective condition formula(OCF), whose value measured the coincidence level of task object. The mathematical model was built by maximum value of OCF and task area. The solution could be obtained by improved genetic algorithm(IGSEGA), which selected the best subcompartments from the initial small class collection, and obtained the most optimal small class collection.[Result] The parameters of OCF were set with task requirement. In research area, the task area was 300 hm2, upper limit as 5% and other conditions. The parameters of GA were as following:gene crossover probability as 0.6, gene variation rate as 0.3, gene variable-length coefficient as 3, iterations as 100. The initial radius as 1 407 m was acquired by ACA, and the radius of expansion was only one time to construct the initial small class collection. Analytical efficiency of general point buffer was lower than ACA because of the uncertainty of initial radius and steps. The initial subcompartment collection could be generated through 14 to 15 iterations because the initial adaptive value was close to the optimal solution by IGSEGA, and the efficiency of solving was higher than the ordinary SGA. The center point of forestry station, 40 subcompartments were obtained and conformed to the objective value. This experiment results showed that the IGSEGA is intelligent and effective.[Conclusion] This paper proposed a concept of forest thinning subcompartment intelligent selection, and constructed the OCF with urgency indicator, difficulty indicator and site indicator. The mathematical model of subcompartment selection was constructed and solved by IGSEGA. Analytical efficiency of buffer analysis was greatly improved by ACA. The research designed a new genetic algorithm encoding with greedy strategy and its genetic operator. It provided an effective method and technology for the concept of forest thinning subcompartment intelligent selection, and decision support for the follow-up forest management activities.