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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles containing the keyword 'Particle Swarm Optimization'.

Category: Research article

article id 201, category Research article
Rui Qi, Véronique Letort, Mengzhen Kang, Paul-Henry Cournède, Philippe de Reffye, Thierry Fourcaud. (2009). Application of the GreenLab model to simulate and optimize wood production and tree stability: a theoretical study. Silva Fennica vol. 43 no. 3 article id 201. https://doi.org/10.14214/sf.201
The GreenLab model was used to study the interaction between source-sink dynamics at the whole tree level, wood production and distribution within the stem, and tree mechanical stability through simulation and optimization. In this first promising numerical attempt, two GreenLab parameters were considered in order to maximize wood production: the sink strength for cambial growth and a coefficient that determines the way the biomass assigned to cambial growth is allocated to each metamer, through optimization and simulation respectively. The optimization procedure that has been used is based on a heuristic optimization algorithm called Particle Swarm Optimization (PSO). In the first part of the paper, wood production was maximized without considering the effect of wood distribution on tree mechanical stability. Contrary to common idea that increasing sink strength for cambial growth leads to increasing wood production, an optimal value can be found. The optimization results implied that an optimal source and sink balance should be considered to optimize wood production. In a further step, the mechanical stability of trees submitted to their self weight was taken into account based on simplified mechanical assumptions. Simulation results revealed that the allocation of wood at the stem base strongly influenced its global deformation. Such basic mechanical criterion can be an indicator of wood quality if we consider further the active biomechanical processes involved in tree gravitropic responses, e.g. formation of reaction wood.
  • Qi, Ecole Centrale Paris, Laboratory of Applied Mathematics, Grande Voie des Vignes, 92295 Chatenay-Malabry, France; Institute of Automation, Chinese Academy of Sciences, LIAMA/NLPR, P.O.Box 2728, Beijing, China ORCID ID:E-mail: qiruitree@gmail.com (email)
  • Letort, Ecole Centrale Paris, Laboratory of Applied Mathematics, Grande Voie des Vignes, 92295 Chatenay-Malabry, France ORCID ID:E-mail:
  • Kang, Institute of Automation, Chinese Academy of Sciences, LIAMA/NLPR, P.O.Box 2728, Beijing, China ORCID ID:E-mail:
  • Cournède, Ecole Centrale Paris, Laboratory of Applied Mathematics, Grande Voie des Vignes, 92295 Chatenay-Malabry, France; INRIA saclay Ile-de-France, EPI Digiplant, Parc Orsay Université, 91893 Orsay cedex, France ORCID ID:E-mail:
  • Reffye, INRIA saclay Ile-de-France, EPI Digiplant, Parc Orsay Université, 91893 Orsay cedex, France; CIRAD, UMR AMAP, Montpellier, F-34000 France ORCID ID:E-mail:
  • Fourcaud, CIRAD, UMR AMAP, Montpellier, F-34000 France ORCID ID:E-mail:
article id 211, category Research article
Timo Pukkala. (2009). Population-based methods in the optimization of stand management. Silva Fennica vol. 43 no. 2 article id 211. https://doi.org/10.14214/sf.211
In Finland, the growth and yield models for tree stands are simulation programs that consist of several sub-models. These models are often non-smooth and non-differentiable. Direct search methods such as the Hooke-Jeeves algorithm (HJ) are suitable tools for optimizing stand management with this kind of complicated models. This study tested a new class of direct search methods, namely population-based methods, in the optimization of stand management. The tested methods were differential evolution, particle swarm optimization, evolution strategy, and the Nelder-Mead method. All these methods operate with a population of solution vectors, which are recombined and mutated to obtain new candidate solutions. The management schedule of 719 stands was optimized with all population-based methods and with the HJ method. The population-based methods were competitive with the HJ method, producing 0.57% to 1.74% higher mean objective function values than HJ. On the average, differential evolution was the best method, followed by particle swarm optimization, evolution strategy, and Nelder-Mead method. However, differences between the methods were small, and each method was the best in several stands. HJ was alone the best method in 7% of stands, and a population based method in 3% (Nelder-Mead) to 29% (differential evolution) of stands. All five methods found the same solution in 18% of stands.
  • Pukkala, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: timo.pukkala@joensuu.fi (email)

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