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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
Multi-Objective Optimization of Natural
Secondary Forest Stand Mixing Degree
Using Particle Swarm Algorithm
1
School of Information Engineering, Hunan Applied Technology University, Changde, 415000, China
2
College of Forestry, Central South University of Forestry and Technology, Changsha, 410000, China
3
Furong College, Hunan University of Science and Arts, Changde, 415000, China
Submission date: 2025-02-13
Final revision date: 2025-06-01
Acceptance date: 2025-08-03
Online publication date: 2025-09-24
Corresponding author
Dongsheng Qing
1School of Information Engineering, Hunan Applied Technology University, Changde, 415000, China
1School of Information Engineering, Hunan Applied Technology University, Changde, 415000, China
Qiaoling Deng
School of Information Engineering, Hunan Applied Technology University, Changde, 415000, China
School of Information Engineering, Hunan Applied Technology University, Changde, 415000, China
KEYWORDS
particle swarm algorithmnatural secondary forestoptimization of mixed degreeforest spatial structuremulti-objective optimization
TOPICS
ABSTRACT
In order to study the performance of the particle swarm optimization (PSO) algorithm in optimizing
the mixing degree of forest stands, this study constructs an optimization model for the mixing degree
of natural secondary forest stands based on PSO. The secondary mixed forest in Hupingshan Nature
Reserve, Hunan Province, was used as a case study to explore the optimization effect under different
cutting intensities (5%, 10%, 15%). The results showed that the mixing degree and fitness of forest
stands increased nonlinearly with the increase of cutting intensity, and the uniformity of mixing degree
distribution was significantly improved. At a small scale, PSO reduces the running time by 98.8%
(2.70 seconds vs. 239.67 seconds) compared to the mixed integer programming (MIP) method, with
an optimal solution achievement rate of 70% and no significant difference in solution quality between
PSO and MIP. In medium to large-scale scenarios, the convergence time of PSO is 41.5%-50.9% shorter
than that of the genetic algorithm (GA) and artificial bee colony (ABC) algorithm, and the number
of iterations is reduced by 21.3%. This confirms that PSO can achieve both optimization accuracy
and efficient computational performance in solving forest mixing degree optimization problems.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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