Jukka Selander, Matti Nuorteva. (1980). Feromonivalmisteen käyttö kirjanpainajien torjumiseksi kuolevassa kuusikossa. Silva Fennica vol. 14 no. 2 article id 5074. https://doi.org/10.14214/sf.a15015

English title: Use of synthetic pheromones for the control of spruce bark beetle in a heavily infested Norway spruce stand.

Original keywords: kuusi; kaarnakuoriaiset; torjunta; hyönteistuho; kirjanpainaja; feromonit

English keywords: bark beetles; insect damages; prevention; Ips typographus; Ips duplicatus; pheromones

Abstract | View details | Full text in PDF | Author Info

The dying-off of more trees in an over-aged Norway spruce (Picea abies (L.) H. Karst.) stand caused by Ips-bark beetles was reduced by a pheromone preparation, ipslure. 20 preparations placed in trapping bolts captured more than 13,700 specimens of Ips typographus L. and Ips duplicatus Sahlb., which alone corresponded to a saving of five old trees in this valuable exhibition and seed collection stand. Attractance of ipslure to the following predators of bark beetles was also examined; Thanasimus formicarius, T. rufipes, Epuracea bickhardti, Rhizophagus ferrugineus, Pityophagus ferrugineus.

The PDF includes a summary in English.

Selander, E-mail: js@mm.unknown
Nuorteva, E-mail: mn@mm.unknown

Category : Research article

article id 396, category Research article

Timo Pukkala, Mikko Kurttila. (2005). Examining the performance of six heuristic optimisation techniques in different forest planning problems. Silva Fennica vol. 39 no. 1 article id 396. https://doi.org/10.14214/sf.396

Keywords: genetic algorithms; simulated annealing; ecological planning; habitat suitability index (HSI); Hero; random search; tabu search

Abstract | View details | Full text in PDF | Author Info

The existence of multiple decision-makers and goals, spatial and non-linear forest management objectives and the combinatorial nature of forest planning problems are reasons that support the use of heuristic optimisation algorithms in forest planning instead of the more traditional LP methods. A heuristic is a search algorithm that does not necessarily find the global optimum but it can produce relatively good solutions within reasonable time. The performance of different heuristics may vary depending on the complexity of the planning problem. This study tested six heuristic optimisation techniques in five different, increasingly difficult planning problems. The heuristics were evaluated with respect to the objective function value that the techniques were able to find, and the time they consumed in the optimisation process. The tested optimisation techniques were 1) random ascent (RA), 2) Hero sequential ascent technique (Hero), 3) simulated annealing (SA), 4) a hybrid of SA and Hero (SA+Hero), 5) tabu search (TS) and 6) genetic algorithm (GA). The results, calculated as averages of 100 repeated optimisations, were very similar for all heuristics with respect to the objective function value but the time consumption of the heuristics varied considerably. During the time the slowest techniques (SA or GA) required for convergence, the optimisation could have been repeated about 200 times with the fastest technique (Hero). The SA+Hero and SA techniques found the best solutions for non-spatial planning problems, while GA was the best in the most difficult problems. The results suggest that, especially in spatial planning problems, it is a benefit if the method performs more complicated moves than selecting one of the neighbouring solutions. It may also be beneficial to combine two or more heuristic techniques.

Pukkala, University of Joensuu, Faculty of Forestry, P.O. BOX 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@forest.joensuu.fi
Kurttila, Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: mk@nn.fi

article id 419, category Research article

Tero Heinonen, Timo Pukkala. (2004). A comparison of one- and two-compartment neighbourhoods in heuristic search with spatial forest management goals. Silva Fennica vol. 38 no. 3 article id 419. https://doi.org/10.14214/sf.419

Keywords: simulated annealing; Hero; tabu search; 2-optimal heuristic; spatial optimisation; random ascent

Abstract | View details | Full text in PDF | Author Info

This study presents a comparison of the performance of four heuristic techniques with one- and two-compartment neighbourhoods in harvest scheduling problems including a spatial objective variable. The tested heuristics were random ascent, Hero, simulated annealing and tabu search. All methods seek better solutions by inspecting the neighbourhood solutions, which are combinations that can be obtained by changing the treatment schedule in one (one-compartment neighbourhood) or two (two-compartment neighbourhood) compartments. The methods and neighbourhoods were examined in one artificial and four real landscapes ranging from 700 to 981 ha in size. The landscapes had 608 to 900 stand compartments, and the examined planning problems had 2986 to 4773 binary decision variables. The objective function was a multi-objective utility function. The spatial objective variable was the percentage of compartment boundary that joins two compartments, both of which are to be cut during the same 20-year period. The non-spatial objectives were net incomes of three consecutive 20-year management periods and the remaining growing stock volume at the end of the third 20-year period. In another problem formulation, the total harvest of the first 20-year period was used as an objective variable together with the spatial objective. The results showed that a two-compartment neighbourhood was systematically and often clearly better than a one-compartment neighbourhood. The improvements were greatest with the simplest heuristics, random ascent and Hero. Of the four heuristics, tabu search and simulated annealing proved to be the best methods, but with a two-compartment neighbourhood the differences between methods were negligible.

Heinonen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: th@nn.fi
Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@joensuu.fi

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