Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

  • Y. Shan Department of Statistics, University of South Carolina, Columbia.
  • P. Bettinger School of Forestry and Natural Resources, University of Georgia, Athens.
  • C. Cieszewski School of Forestry and Natural Resources, University of Georgia, Athens.
  • W. Wang Biological & Agricultural Engineering, University of Georgia, Tifton.


We describe here an example of applying particle swarm optimization (PSO) — a population-based heuristic technique — to maximize the net present value of a contemporary southern United States forest plan that includes spatial constraints (green-up and adjacency) and wood flow constraints. When initiated with randomly defined feasible initial conditions, and tuned with some appropriate modifications, the PSO algorithm gradually converged upon its final solution and provided reasonable objective function values. However, only 86% of the global optimal value could be achieved using the modified PSO heuristic. The results of this study suggest that under random-start initial population conditions the PSO heuristic may have rather limited application to forest planning problems with economic objectives, wood-flow constraints, and spatial considerations. Pitfalls include the need to modify the structure of PSO to both address spatial constraints and to repair particles, and the need to modify some of the basic assumptions of PSO to better address contemporary forest planning problems. Our results, and hence our contributions, are contrary to earlier work that illustrated the impressive potential of PSO when applied to stand-level forest planning problems or when applied to a high quality initial population.


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How to Cite
ShanY., BettingerP., CieszewskiC., & WangW. (2012). Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning. Forest Systems, 21(3), 468-480. https://doi.org/10.5424/fs/2012213-03692
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