Simulated Annealing Search
Pete Bettinger
Additional contact information
Pete Bettinger: University of Georgia
Chapter Chapter 9 in Forest Harvest Scheduling, 2025, pp 179-195 from Springer
Abstract:
Abstract Simulated annealing is a heuristic search process that is very similar to threshold accepting, with one key difference that is associated with the acceptance rule employed when an inferior solution is proposed. As with threshold accepting, simulated annealing might be considered a refinement of Monte Carlo simulation. During the search process, a single solution is maintained, and a random perturbation is proposed as a change to it (Form 4 in Chap. 7 ). Should the proposed change result in a higher quality, feasible solution, it is automatically accepted. However, should the change result in an inferior solution, it may be acceptable based on a decision that varies by (a) how distant the objective function value of the proposed solution is from the objective function value of the previous (or best) solutions, and (b) how long the heuristic search has been functioning. Some enhancements to the basic structure of a simulated annealing search process allow it to produce high quality results for forest harvest scheduling problems.
Keywords: Heuristic; Combinatorial optimization; s-metaheuristic (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-031-89432-9_9
Ordering information: This item can be ordered from
http://www.springer.com/9783031894329
DOI: 10.1007/978-3-031-89432-9_9
Access Statistics for this chapter
More chapters in Springer Texts in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().