Comparison of population-based algorithms for optimizing thinnings and rotation using a process-based growth model

Hailian Xue, Aino Annikki Mäkelä-Carter, Jerome Vanclay, Lauri Tapani Valsta, Tianjian Cao

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Stand management optimization has long been computationally demanding as increasingly detailed growth and yield models have been developed. Process-based growth models are useful tools for predicting forest dynamics. However, the difficulty of classic optimization algorithms limited its applications in forest planning. This study assessed alternative approaches to optimizing thinning regimes and rotation length using a process-based growth model. We considered (1) population-based algorithms proposed for stand management optimization, including differential evolution (DE), particle swarm optimization (PSO), evolution strategy (ES), and (2) derivative-free search algorithms, including the Nelder–Mead method (NM) and Osyczka’s direct and random search algorithm (DRS). We incorporated population-based algorithms into the simulation-optimization system OptiFor in which the process-based model PipeQual was the simulator. The results showed that DE was the most reliable algorithm among those tested. Meanwhile, DRS was also an effective algorithm for sparse stands with fewer decision variables. PSO resulted in some higher objective function values, however, the computational time of PSO was the longest. In general, of the population-based algorithms, DE is superior to the competing ones. The effectiveness of DE for stand management optimization is promising and manifested.
Original languageEnglish
JournalScandinavian Journal of Forest Research
Volume34
Issue number6
Pages (from-to)458-468
Number of pages11
ISSN0282-7581
DOIs
Publication statusPublished - 26 Feb 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 4112 Forestry

Cite this

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title = "Comparison of population-based algorithms for optimizing thinnings and rotation using a process-based growth model",
abstract = "Stand management optimization has long been computationally demanding as increasingly detailed growth and yield models have been developed. Process-based growth models are useful tools for predicting forest dynamics. However, the difficulty of classic optimization algorithms limited its applications in forest planning. This study assessed alternative approaches to optimizing thinning regimes and rotation length using a process-based growth model. We considered (1) population-based algorithms proposed for stand management optimization, including differential evolution (DE), particle swarm optimization (PSO), evolution strategy (ES), and (2) derivative-free search algorithms, including the Nelder–Mead method (NM) and Osyczka’s direct and random search algorithm (DRS). We incorporated population-based algorithms into the simulation-optimization system OptiFor in which the process-based model PipeQual was the simulator. The results showed that DE was the most reliable algorithm among those tested. Meanwhile, DRS was also an effective algorithm for sparse stands with fewer decision variables. PSO resulted in some higher objective function values, however, the computational time of PSO was the longest. In general, of the population-based algorithms, DE is superior to the competing ones. The effectiveness of DE for stand management optimization is promising and manifested.",
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Comparison of population-based algorithms for optimizing thinnings and rotation using a process-based growth model. / Xue, Hailian; Mäkelä-Carter, Aino Annikki; Vanclay, Jerome; Valsta, Lauri Tapani; Cao, Tianjian.

In: Scandinavian Journal of Forest Research, Vol. 34, No. 6, 26.02.2019, p. 458-468.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Comparison of population-based algorithms for optimizing thinnings and rotation using a process-based growth model

AU - Xue, Hailian

AU - Mäkelä-Carter, Aino Annikki

AU - Vanclay, Jerome

AU - Valsta, Lauri Tapani

AU - Cao, Tianjian

PY - 2019/2/26

Y1 - 2019/2/26

N2 - Stand management optimization has long been computationally demanding as increasingly detailed growth and yield models have been developed. Process-based growth models are useful tools for predicting forest dynamics. However, the difficulty of classic optimization algorithms limited its applications in forest planning. This study assessed alternative approaches to optimizing thinning regimes and rotation length using a process-based growth model. We considered (1) population-based algorithms proposed for stand management optimization, including differential evolution (DE), particle swarm optimization (PSO), evolution strategy (ES), and (2) derivative-free search algorithms, including the Nelder–Mead method (NM) and Osyczka’s direct and random search algorithm (DRS). We incorporated population-based algorithms into the simulation-optimization system OptiFor in which the process-based model PipeQual was the simulator. The results showed that DE was the most reliable algorithm among those tested. Meanwhile, DRS was also an effective algorithm for sparse stands with fewer decision variables. PSO resulted in some higher objective function values, however, the computational time of PSO was the longest. In general, of the population-based algorithms, DE is superior to the competing ones. The effectiveness of DE for stand management optimization is promising and manifested.

AB - Stand management optimization has long been computationally demanding as increasingly detailed growth and yield models have been developed. Process-based growth models are useful tools for predicting forest dynamics. However, the difficulty of classic optimization algorithms limited its applications in forest planning. This study assessed alternative approaches to optimizing thinning regimes and rotation length using a process-based growth model. We considered (1) population-based algorithms proposed for stand management optimization, including differential evolution (DE), particle swarm optimization (PSO), evolution strategy (ES), and (2) derivative-free search algorithms, including the Nelder–Mead method (NM) and Osyczka’s direct and random search algorithm (DRS). We incorporated population-based algorithms into the simulation-optimization system OptiFor in which the process-based model PipeQual was the simulator. The results showed that DE was the most reliable algorithm among those tested. Meanwhile, DRS was also an effective algorithm for sparse stands with fewer decision variables. PSO resulted in some higher objective function values, however, the computational time of PSO was the longest. In general, of the population-based algorithms, DE is superior to the competing ones. The effectiveness of DE for stand management optimization is promising and manifested.

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