Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem

Abstract

Search reversion within s-metaheuristics: impacts illustrated with a forest planning problemBettinger P., Demirci M., Boston K. (2015). Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem. Silva Fennica vol. 49 no. 2 article id 1232. 20 p. Highlights• The interruption of the sequence of events used to explore a solution space and develop a forest plan, and the re-initiation of the search process from a high-quality, known starting point (reversion) seems necessary for some s-metaheuristics.• When using a s-metaheuristic, higher quality forest plans may be developed when the reversion interval is around six iterations of the model. AbstractThe use of a reversion technique during the search process of s-metaheuristics has received little attention with respect to forest management and planning problems. Reversion involves the interruption of the sequence of events that are used to explore the solution space and the re-initiation of the search process from a high-quality, known starting point. We explored four reversion rates when applied to three different types of s-metaheuristics that have previously shown promise for the forest planning problem explored, threshold accepting, tabu search, and the raindrop method. For two of the s-metaheuristics, we also explored three types of decision choices, a change to the harvest timing of a single management unit (1-opt move), the swapping of two management unit's harvest timing (2-opt moves), and the swapping of three management unit's harvest timing (3-opt moves). One hundred independent forest plans were developed for each of the metaheuristic / reversion rate combinations, all beginning with randomly-generated feasible starting solutions. We found that (a) reversion does improve the quality of the solutions generated, and (b) the rate of reversion is an important factor that can affect solution quality.

Evaluating the neighborhood, hybrid and reversion search techniques of a simulated annealing algorithm in solving forest spatial harvest scheduling problems

“…The objective function of the forest planning problem involves minimizing the squared deviation between the scheduled harvest volume in each time period and a static target harvest volume. This objective is analogous to the tactical plan and the objective function found in Bettinger et al (2007Bettinger et al ( , 2015.…”

“…However, the main consideration of this process was how to determine the integration points (as denoted by the iteration number) for oscillating the search process from 1-opt moves to 2-opt moves. We employ a fixed breakpoint method which was introduced by Bettinger et al (2015), in which an integration point can be calculated as:…”

“…In addition, Bettinger et al (2002Bettinger et al ( , 2007 introduced an unparalleled search strategy that combined 1-opt and 2-opt moves together, which could be considered as self-hybrid process. Very recently, Bettinger et al (2015) noted that search reversion may also dramatically improve s-metaheuristic results by interrupting the sequence of search process events, and re-initiating the search process from a high-quality solution value. However, the performance of this strategy needs to be assessed on a wider scale and scope of planning problem.…”

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

“…Within the forestry literature, evidence suggests that for problems containing periodic wood flow and harvest adjacency constraints, schedules of activities can be rearranged using search reversion so that significant improvements in objective function values can be obtained [19]. This strategy has been shown to produce better tabu search and threshold accepting results, in nearly every instance, than search strategies that lack a reversion process [19,20]. Therefore, these published results provide a concrete recommendation for s-metaheuristics: utilization of a reversion strategy seems necessary.…”

“…Therefore, these published results provide a concrete recommendation for s-metaheuristics: utilization of a reversion strategy seems necessary. However, an open area of research seems to involve investigation into the rate of reversion during a heuristic search, as prior research [19,20] is somewhat inconsistent in this regard. Further, an open area of research would also seem to involve investigation into whether the reversion process should be employed with a systematic or random rate of occurrence.…”