Examining the performance of six heuristic optimisation techniques in different forest planning problems

Abstract

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.

Uncertainty in forest simulators and forest planning systems

“…Many FPSs also include an optimisation model that is used for choosing the optimal management activities from a group of alternative simulated treatment schedules. The optimisation models in various FPSs have been either exact, such as linear programming algorithms, or heuristic algorithms (Pukkala & Kangas 1993, Pukkala & Kurttila 2005, Heinonen 2007. FPSs commonly have different types of user interfaces and interfaces with GIS.…”

A Direct Local Search Method and its Application to a Markovian Model

“…A possible reason is that GA appears to perform well in very difficult problems, and not well enough in simple ones, as suggested by Pukkala & Kurttila [18]. The simulated annealing, L-BFGS-B, and differential evolution performed rather well, but not better than PSO and NM.…”

Heuristic forest planning model for optimizing timber production and carbon sequestration in teak plantations

“…The SA technique has been previously applied to forest harvest planning (Pukkala & Kurttila 2005, Liu et al 2006, Quintero et al 2010, and has proven to achieve solutions very close to a mathematical optimum. The SA algorithm uses random initial solutions and a geometric cooling schedule.…”

“…The large number of variables and constraints involved, the existence of non-linear relationships between variables, and the need to simultaneously optimize for different objectives often make the optimization of models an arduous task. In this case, heuristic techniques can be applied as they can handle the model complexity more efficiently (Bettinger & Chung 2004, Pukkala & Kurttila 2005, Liu et al 2006. Among these techniques, Simulated Annealing, Genetic Algorithms, Tabu Search, Threshold Accepting, and Ant Colony Algorithms were successfully applied in forest planning (Pukkala & Kurttila 2005, Liu et al 2006, Zeng et al 2007, Zhu & Bettinger 2008, Quintero et al 2011.…”