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研究报告目录 |
Optimisation algorithms for spatially constrained forest planning |
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| 出 版 社：ecological modelling x x x ( 2 0 0 5 ) xxx–xxx |
| 发表时间：2005 |
| 台 站：
长白山森林生态系统定位研究站
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| 作 者：Guoliang Liu, Shijie Han, Xiuhai Zhao, John D. Nelsonc, Hongshu Wang,Weiying Wang, |
| 点 击 率：6536 |
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| 关 键 字：Landscape design ,Integrated forest resource planning,Harvest scheduling,Genetic algorithms,Simulated annealing |
| 摘 要：a b s t r a c t
We 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. Eachmethod 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.
© 2005 Elsevier B.V. All rights reserved. |
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