Application of ant colony optimization for the risk management of wind damage in forest planning

Silva Fennica

Volumn 41, Issue 2, 2007, Pages 315-332

  Zeng, Hongcheng ^a   Pukkala, Timo ^a   Peltola, Heli ^a   Kellomäki, Seppo ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

Genetic algorithms; Harvest scheduling; Heuristics; Simulated annealing; Spatial optimization

Indexed keywords

FORESTRY; GENETIC ALGORITHMS; HEURISTIC METHODS; RISK MANAGEMENT; SIMULATED ANNEALING;

ANT COLONY OPTIMIZATION (ACO); CUT PATTERNS; FOREST LANDSCAPE; HARVEST SCHEDULING; SPATIAL OPTIMIZATION; WIND DAMAGE;

WIND EFFECTS;

CLEARCUTTING; DAMAGE; FOREST MANAGEMENT; GENETIC ALGORITHM; HEURISTICS; OPTIMIZATION; PLANNING METHOD; RISK ASSESSMENT; SIMULATED ANNEALING; TIMBER HARVESTING; WIND FORCING;

FORESTRY; GENETICS; HEURISTIC METHODS; RISK MANAGEMENT; SIMULATION; WIND;

EURASIA; EUROPE; FINLAND; NORTHERN EUROPE; SCANDINAVIA;

EID: 34250805348     PISSN: 00375330     EISSN: None     Source Type: Journal    
DOI: 10.14214/sf.299     Document Type: Article

References (33)

1. Bauer, A., Bullnheimer, B., Harti, R.F. & Strauss, C. 1999. An ant colony optimization approach for the single machine total tardiness problem. In: Proc. Congr. Evolutionary Computation (CEC'99). IEEE Press, Piscataway, NJ. p. 1445-1450.
2. Bettinger, P., Sessions, J. & Boston, K. 1997. Using Tabu search to schedule timber harvest subject to spatial wildlife goals for big game. Ecological Modeling 94: 111-123.
3. _ , Boston, K. & Sessions, J. 1999. Combinatorial optimization of elk habitat effectiveness and timber harvest volume. Environmental Modeling and Assessment 4: 143-153.
4. _ , Graetz, D., Boston, K., Sessions, J. & Chung, W. 2002. Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems. Silva Fennica 36(2): 561-584.
5. Bonabeau, E., Dorigo, M. & Theraulaz, G. 2000. Inspiration for optimization from social insect behaviour. Nature 406: 39-42.
6. Boston, K. & Bettinger, P. 1999. An analysis of Monte Carlo integer programming, simulated annealing and tabu search for solving spatial harvest scheduling problems. Forest Science 45: 292-301.
7. _ & Bettinger, P. 2002. Combining Tabu search and genetic algorithms heuristic techniques to solve spatial harvest schedule problems. Forest Science 48(1): 35-46.
8. Borges, J.G., Hoganson, H.M. & Falcao, A.O. 2002. Heuristics in multi-objective forest planning. In: Pukkala, T. (ed.). Multi-objective forest planning. Kluwer Academic Publishers, Dordrecht. p. 119-151.
9. Colorni, A., Dorigo, M. & Maniezzo, V. 1991. Distribution optimization by ant colonies. In: Varela, F. & Bourgine, P. (eds.). Proc. Conf. Artificial Life. Paris, France. Elsevier. p. 134-142.
10. Crowe, K. & Nelson, J. 2003. An indirect search algorithm for harvest-scheduling under adjacency constraints. Forest Science 49(1): 1-11.
11. Dahlin, B. & Sallnäs, O. 1993. Harvest scheduling under adjacency constraints - a planning problem study from the Swedish sub-alpine region. Scandinavian Journal of Forest Research 8: 281-290.
12. Dorigo, M. & Gambardella, L.M. 1997. Ant colonies for the traveling salesman problem. BioSystems 43: 73-81.
13. _ , Maiezzo, V. & Colorni, A. 1996. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 26(1): 29-41.
14. ESRI. 2006. ArcGIS desktop - general concepts forum. [Internet site]. Available at: http://forums.esri.com/Thread.asp?c=93&f=982&t= 58595&mc=23#msgid228147. [Cited 20 Feb 2006].
15. Falcao, A.O. & Borges, J. 2002. Combining random and systematic search heuristic procedures for solving spatially constrained forest management scheduling models. Forest Science 48(3): 608-621.
16. FragStats. 2006. FragStats documentation. [Online document]. Available at: http://www.umass.edu/landeco/research/fragstats/documents/ fragstats_documents.html. [Cited 14 Nov 2006].
17. Heinonen, T. & Pukkala, T. 2004. A comparison of one- and two-compartment neighbourhoods in heuristic search with spatial forest management goals. Silva Fennica 38(3): 319-332.
18. Kellomäki, S., Väisänen, H., Hänninen, H., Kolström, T., Lauhanen, R., Mattila, U. & Pajari, B. 1992. Sima: a model for forest succession based on the carbon and nitrogen cycles with application to silvicultural management of the forest ecosystem. Silva Carelica 22: 1-91.
19. Lockwood, C. & Moore, T. 1993. Harvest scheduling with spatial constraints, a simulated annealing approach. Canadian Journal of Forest Research 23: 468-478.
20. Lu, F. & Eriksson, L.O. 2000. Formation of harvest units with genetic algorithms. Forest Ecology and Management 130: 57-67.
21. Maniezzo, V. & Colorni, A. 1999. The ant system applied to the quadratic assignment problem. IEEE Trans. Knowledge Data Engin. 11: 769-778.
22. Merkle, D., Middendorf, M. & Schmeck, H. 2000. Pheromone evaluation in ant colony optimization. Industrial electronics society. 26th annual conference of the IEEE. 4. p. 2726-2731.
23. Öhman, K. 2000. Creating continuous areas of old forest in long-term forest planning. Canadian Journal of Forest Research 19: 715-724.
24. Pellikka, P. & Järvenpää, E. 2003. Forest stand characteristics and wind and snow induced forest damage in boreal forest. Proceedings of the International Conference on Wind Effects on Trees, held in September 16-18, 2003, University of Karlsruhe, Germany. p. 8.
25. Peltola, H., Kellomäki, S., Väisänen, H. & Ikonen, V.P. 1999. A mechanistic model for assessing the risk of wind and snow damage to single trees and stands of Scots pine, Norway spruce, and birch. Canadian Journal of Forest Research 29: 647-661.
26. Pukkala T. & Heinonen, T. 2006. Optimizing heuristic search in forest planning. Nonlinear Analysis: Real World Applications 7: 1284-1297.
27. _ & Kurttila, M. 2005. Examining the performance of six heuristic optimisation techniques in different forest planning problems. Silva Fennica 39(1): 67-80.
28. Sun, J., Xiong, S.W. & Guo, F.M. 2004. A new pheromone updating strategy in ant colony optimization. Proceedings of the third international conference on machine learning and cybernetics. Shanghai, 26-29 Aug. p. 620-625.
29. Talkkari, A., Peltola, H., Kellomäki, S. & Strandman, H., 2000. Integration of component models from the tree, stand and regional levels to assess the risk of wind damage at forest margins. Forest Ecology and Management 135: 303-313.
30. Venäläinen, A., Zeng, H., Peltola, A., Talkkari, A., Strandman, H., Wang, K. & Kellomäki, S. 2004. Simulation of the influence of forest management on wind climate on a regional scale. Agricultural and Forest Meterorology 123: 149-158.
31. Zeng, H., Peltola, H., Talkkari, A., Venäläinen, A., Strandman, H., Kellomäki, S. & Wang, K. 2004. Influence of clear-cutting on the risk of wind damage at forest edges. Forest Ecology and Management 203: 77-88.
32. _ , Peltola, H., Talkkari, A., Strandman, H., Venäläinen, A., Wang, K. & Kellomäki, S. 2006. Simulations of the influence of clear-cuttings on the risk of wind damage on a regional scale over a 20 year period. Canadian Journal of Forest Research 36: 2247-2258.
33. _ , Pukkala, T. & Peltola, H. 2007. The use of heuristic optimization in risk management of wind damage in forest planning. Forest Ecology and Management 241: 189-199.