Optimisation algorithms for spatially constrained forest planning
Liu, GL; Han, SJ; Zhao, XH; Nelson, JD; Wang, HS; Wang, WY
Corresponding AuthorLiu, GL(
AbstractWe compared genetic algorithms, simulated annealing and hill climbing algorithms on spatially constrained, integrated forest planning problems. There has been growing interest in algorithms that mimic natural processes, such as genetic algorithms and simulated annealing. These algorithms use random moves to generate new solutions, and employ a probabilistic acceptance/rejection criterion that allows inferior moves within the search space. Algorithms for a genetic algorithm, simulated annealing, and random hill climbing are formulated and tested on a same-sample forest-planning problem where the adjacency rule is strictly enforced. Each method was randomly started 20 times and allowed to run for 10,000 iterations. All three algorithms identified good solutions (within 3% of the highest found), however, simulated annealing consistently produced superior solutions. Simulated annealing and random hill climbing were approximately 10 times faster than the genetic algorithm because only one solution needs to be modified at each iteration. Performance of simulated annealing was essentially independent of the starting point, giving it an important advantage over random hill climbing. The genetic algorithm was not well suited to the strict adjacency problem because considerable computation time was necessary to repair the damage caused during crossover. (c) 2005 Elsevier B.V. All rights reserved.
Keywordlandscape design integrated forest resource planning harvest scheduling genetic algorithms simulated annealing
Indexed BySCI
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEcology
WOS IDWOS:000236693500009
Citation statistics
Cited Times:30[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorLiu, GL
Affiliation1.Chinese Acad Sci, Inst Appl Forest Ecol, Shenyang, Peoples R China
2.Beijing Forestry Univ, Key Lab Silviculture & Conservat, Minist Educ, Beijing, Peoples R China
3.Univ British Columbia, Fac Forestry, Vancouver, BC V6T 1Z4, Canada
Recommended Citation
GB/T 7714
Liu, GL,Han, SJ,Zhao, XH,et al. Optimisation algorithms for spatially constrained forest planning[J]. ECOLOGICAL MODELLING,2006,194(4):421-428.
APA Liu, GL,Han, SJ,Zhao, XH,Nelson, JD,Wang, HS,&Wang, WY.(2006).Optimisation algorithms for spatially constrained forest planning.ECOLOGICAL MODELLING,194(4),421-428.
MLA Liu, GL,et al."Optimisation algorithms for spatially constrained forest planning".ECOLOGICAL MODELLING 194.4(2006):421-428.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, GL]'s Articles
[Han, SJ]'s Articles
[Zhao, XH]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, GL]'s Articles
[Han, SJ]'s Articles
[Zhao, XH]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, GL]'s Articles
[Han, SJ]'s Articles
[Zhao, XH]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.