Acceleration and enhancement of reliability of simulated annealing for optimizing thinning schedule of a forest stand

Abstract

Although a simulated-annealing (SA) based method for exploring the optimal thinning schedule in a forest stand has been developed, its long calculation time limits its applicability. Furthermore, the method occasionally fails to provide a sufficient optimal solution even with multiple iterations. Therefore, this study aims to accelerate and enhance the reliability of the aforementioned method. To enhance the reliability, a new neighborhood search method and meta-optimization technique of the parameter sets of the SA were developed. To accelerate the calculation, the technique of general-purpose computing on GPU (GPGPU) was utilized. Using 16 harvesting models involving 20 rotation ages, an effective implementation was identified. Although the ordinary method occasionally failed to provide solutions that exceeded the quality of the solutions provided using a full-search method, the new neighborhood search method provided better solutions with reasonable number of iterations for all cases. The GPGPU allowed up to 28 times acceleration with a GPU, compared with a program that uses only one 28-thread CPU. For a model with 20 candidate rotation ages, the developed method can provide practically sufficient optimal solutions within 6 s for a simple yielding model and within 1 min for a complex logging yielding model.