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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles containing the keyword 'reversion search'.

Category: Research article

article id 1622, category Research article
Lingbo Dong, Pete Bettinger, Zhaogang Liu, Huiyan Qin, Yinghui Zhao. (2016). Evaluating the neighborhood, hybrid and reversion search techniques of a simulated annealing algorithm in solving forest spatial harvest scheduling problems. Silva Fennica vol. 50 no. 4 article id 1622. https://doi.org/10.14214/sf.1622
Highlights: The performances of neighborhood, hybrid and reversion search strategies of simulated annealing were evaluated when implemented with a forest spatial harvest scheduling problem; The performances of alternative search strategies of simulated annealing were all systematic and clear better than the conventional strategy; The reversion techniques were significant superior to the other search strategies in solving forest spatial harvest scheduling problems.

Heuristic techniques have been increasingly used to address the complex forest planning problems over the last few decades. However, heuristics only can provide acceptable solutions to difficult problems, rather than guarantee that the optimal solution will be located. The strategies of neighborhood, hybrid and reversion search processes have been proved to be effective in improving the quality of heuristic results, as suggested recently in the literature. The overall aims of this paper were therefore to systematically evaluate the performances of these enhanced techniques when implemented with a simulated annealing algorithm. Five enhanced techniques were developed using different strategies for generating candidate solutions. These were then compared to the conventional search strategy that employed 1-opt moves (Strategy 1) alone. The five search strategies are classified into three categories: i) neighborhood search techniques that only used the change version of 2-opt moves (Strategy 2); ii) self-hybrid search techniques that oscillate between 1-opt moves and the change version of 2-opt moves (Strategy 3), or the exchange version of 2-opt moves (Strategy 4); iii) reversion search techniques that utilize 1-opt moves and the change version of 2-opt moves (Strategy 5) or the exchange version of 2-opt moves (Strategy 6). We found that the performances of all the enhanced search techniques of simulated annealing were systematic and often clear better than conventional search strategy, however the required computational time was significantly increased. For a minimization planning problem, Strategy 6 produced the lowest mean objective function values, which were less than 1% of the means developed using conventional search strategy. Although Strategy 6 performed very well, the other search strategies should not be neglected because they also have the potential to produce high-quality solutions.

  • Dong, Department of Forest Management, College of Forestry, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: farrell0503@126.com
  • Bettinger, Warnell School of Forestry and Natural Resources, University of Georgia, Athens 30602, GA, USA ORCID ID:E-mail: pbettinger@warnell.uga.edu
  • Liu, Department of Forest Management, College of Forestry, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: lzg19700602@163.com (email)
  • Qin, Department of Forestry Economic, College of Economic & Management, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: huiyanqin@hotmail.com
  • Zhao, Department of Forest Management, College of Forestry, Northeast Forestry University, Harbin 150040, China ORCID ID:E-mail: zyinghui0925@126.com

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