Population-based methods in the optimization of stand management.
By T. Pukkala. 2009
59 citations recorded by the source corpus.
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.
Subfields: Environmental Engineering, Global and Planetary Change, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/1edacf61703b8810f07193e9fa41e0cd5b55b8f0
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In Environmental Engineering
In Global and Planetary Change
In Nature and Landscape Conservation
Citations in the corpus
- Optimizing the structure and management of uneven-sized stands of Finland (2010, methodology): “All the above optimization methods were used exactly in the same way as explained in Pukkala (2009).”