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Fine-tuning heuristic methods for combinatorial optimization in forest planning

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Abstract

Heuristic methods are commonly used in complicated spatial forest planning problems to find the best combination of management alternatives for stands. The performance of heuristic methods depends on the parameters that guide their search processes. This study used numerical optimization to find the optimal parameter values for simulated annealing (SA), threshold accepting (TA), great deluge (GD), tabu search (TS), genetic algorithm (GA) and ant colony optimization (AC) when they are used for combinatorial optimization in forest planning. Ant colony optimization was implemented using the Max–Min Ant System, which was applied for the first time to forest planning problem. Solutions found by different heuristic methods for a non-spatial and a spatial forest planning problem were compared in a situation where the search time was restricted. The comparisons revealed that SA and TA were the best methods for fast search in both non-spatial and spatial problems. GA and AC were the least satisfactory methods, and GD and TS were between the best and the worst heuristics. The main reason for the poor performance of GA and AC was their slow search process. Differences between heuristic methods decreased when the allowed search time increased.

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Acknowledgments

This research was financially supported by the Ministry of Science and Technology of the People’s Republic of China (2015BAD09B01) and the Fundamental Research Funds for the Central Universities of the People’s Republic of China (2572014BA09). The authors thank the teachers and students of the Department of Forest Management, Northeast Forestry University (NEFU), PR China, who collected the data for this study.

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Authors and Affiliations

  1. Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin, 150040, Heilongjiang, People’s Republic of China

    Xingji Jin & Fengri Li

  2. University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland

    Timo Pukkala

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  1. Xingji Jin
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  2. Timo Pukkala
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  3. Fengri Li
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Correspondence to Fengri Li.

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Communicated by Martin Moog.

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Jin, X., Pukkala, T. & Li, F. Fine-tuning heuristic methods for combinatorial optimization in forest planning. Eur J Forest Res 135, 765–779 (2016). https://doi.org/10.1007/s10342-016-0971-x

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  • DOI: https://doi.org/10.1007/s10342-016-0971-x

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