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

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

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

Kuvaus

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.
Alkuperäiskielienglanti
LehtiScandinavian Journal of Forest Research
Vuosikerta34
Numero6
Sivut458-468
Sivumäärä11
ISSN0282-7581
DOI - pysyväislinkit
TilaJulkaistu - 18 elokuuta 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Tieteenalat

  • 4112 Metsätiede

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@article{b98c9858235f450fa8a6c222d18f3d81,
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.",
keywords = "4112 Forestry, Algorithm performance, optimal thinning, population-based algorithms, process-based model, CARBON-BALANCE, STAND MANAGEMENT, SIMPLEX-METHOD, OPTIMIZATION, PRODUCTIVITY, SEARCH, PINE",
author = "Hailian Xue and M{\"a}kel{\"a}-Carter, {Aino Annikki} and Valsta, {Lauri Tapani} and Jerome Vanclay and Tianjian Cao",
year = "2019",
month = "8",
day = "18",
doi = "10.1080/02827581.2019.1581252",
language = "English",
volume = "34",
pages = "458--468",
journal = "Scandinavian Journal of Forest Research",
issn = "0282-7581",
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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; Valsta, Lauri Tapani; Vanclay, Jerome; Cao, Tianjian.

julkaisussa: Scandinavian Journal of Forest Research, Vuosikerta 34, Nro 6, 18.08.2019, s. 458-468.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

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 - Valsta, Lauri Tapani

AU - Vanclay, Jerome

AU - Cao, Tianjian

PY - 2019/8/18

Y1 - 2019/8/18

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.

KW - 4112 Forestry

KW - Algorithm performance

KW - optimal thinning

KW - population-based algorithms

KW - process-based model

KW - CARBON-BALANCE

KW - STAND MANAGEMENT

KW - SIMPLEX-METHOD

KW - OPTIMIZATION

KW - PRODUCTIVITY

KW - SEARCH

KW - PINE

U2 - 10.1080/02827581.2019.1581252

DO - 10.1080/02827581.2019.1581252

M3 - Article

VL - 34

SP - 458

EP - 468

JO - Scandinavian Journal of Forest Research

JF - Scandinavian Journal of Forest Research

SN - 0282-7581

IS - 6

ER -