Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems.

As both spatial and temporal characteristics of desired future conditions are becoming important measures of forest plan success, forest plans and forest planning goals are becoming complex. Heuristic techniques are becoming popular for developing alternative forest plans that include spatial constraints. Eight types of heuristic planning techniques were applied to three increasingly difficult forest planning problems where the objective function sought to maximize the amount of land in certain types of wildlife habitat. The goal of this research was to understand the relative challenges and opportunities each technique presents when more complex difficult goals are desired. The eight heuristic techniques were random search, simulated annealing, great deluge, threshold accepting, tabu search with 1-opt moves, tabu search with 1-opt and 2-opt moves, genetic algorithm, and a hybrid tabu search / genetic algorithm search process. While our results should not be viewed as universal truths, we determined that for the problems we examined, there were three classes of techniques: very good (simulated annealing, threshold accepting, great deluge, tabu search with 1-opt and 2-opt moves, and tabu search / genetic algorithm), adequate (tabu search with 1-opt moves, genetic algorithm), and less than adequate (random search). The relative advantages in terms of solution time and complexity of programming code are discussed and should provide planners and researchers a guide to help match the appropriate technique to their planning problem. The hypothetical landscape model used to evaluate the techniques can also be used by others to further compare their techniques to the ones described here.