Behavior of Search Process

As one can see in Figure 2, which represents a hypothetical simulated annealing search, large declines in incremental solution value are allowed early in the search, yet smaller declines are allowed as the search progresses. The current solution represents the quality of the solution that is being changed. The potential change in the current solution, if lower than the best solution value, is inserted into the annealing function described above, a random number is drawn, and a decision is made. Therefore, the search allows declines in

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iteration of the search process

Figure 2 One example of the current and best solutions located using simulated annealing.

iteration of the search process

Figure 2 One example of the current and best solutions located using simulated annealing.

solution value to avoid becoming trapped in various local optima, and the process is allowed to move through the solution space in search of the best solution possible.

The type of output that can be produced when using a heuristic to develop a management or conservation plan include (1) tabular data that describe the expected ecological and economic impacts of the plan, as well as (2) graphical representations of both the location of proposed activities and the location of expected ecological values (in this case the location of high-quality habitat). Tabular data are instructive, and help one understand, quantitatively, the benefits and costs of scenarios (and differences between scenarios). On the other hand, maps (e.g., Figure 3) help engage people in discussions of the more qualitative nature of management or conservation plans.

Scheduled activities High-quality habitat Landscape stands

Summary

Figure 3 An example output from a management plan showing the high-quality habitat and management activity areas for time period 1 (a) and time period 2 (b).

Simulated annealing is a heuristic solution generation process that relies on logic and rules to iteratively change a suboptimal solution to a problem, and seeks to locate the best solution possible, usually a near-optimal solution. The process is relatively fast, compared to traditional mathematical programming methods as well as other heuristic processes. Adjustments to a solution are chosen randomly from the neighborhood of the existing solution, and if they result in an inferior solution, they may be acceptable, but the probability of acceptance declines as the number of adjustments increase. One can utilize simulated annealing to develop conservation and management plans for large areas. The advantage to using the heuristic process comes when the decisions (land-use activities) assigned to management units are numerous and utilize binary variables.

See also: Boltzman Learning; Forest Management; Hopfield Network.

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