Population-based methods in the optimization of stand management
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...
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| Format: | Article |
| Language: | English |
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Finnish Society of Forest Science
2009-01-01
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| Series: | Silva Fennica |
| Online Access: | https://www.silvafennica.fi/article/211 |
| _version_ | 1818513169369268224 |
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| author | Pukkala, Timo |
| author_facet | Pukkala, Timo |
| author_sort | Pukkala, Timo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T23:57:27Z |
| format | Article |
| id | doaj.art-c1389f042a974f19ad334480a0531711 |
| institution | Directory Open Access Journal |
| issn | 2242-4075 |
| language | English |
| last_indexed | 2024-12-10T23:57:27Z |
| publishDate | 2009-01-01 |
| publisher | Finnish Society of Forest Science |
| record_format | Article |
| series | Silva Fennica |
| spelling | doaj.art-c1389f042a974f19ad334480a05317112022-12-22T01:28:33ZengFinnish Society of Forest ScienceSilva Fennica2242-40752009-01-0143210.14214/sf.211Population-based methods in the optimization of stand managementPukkala, TimoIn 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.https://www.silvafennica.fi/article/211 |
| spellingShingle | Pukkala, Timo Population-based methods in the optimization of stand management Silva Fennica |
| title | Population-based methods in the optimization of stand management |
| title_full | Population-based methods in the optimization of stand management |
| title_fullStr | Population-based methods in the optimization of stand management |
| title_full_unstemmed | Population-based methods in the optimization of stand management |
| title_short | Population-based methods in the optimization of stand management |
| title_sort | population based methods in the optimization of stand management |
| url | https://www.silvafennica.fi/article/211 |
| work_keys_str_mv | AT pukkalatimo populationbasedmethodsintheoptimizationofstandmanagement |