林区公路网评价和优化研究
摘要
可持续发展是人类社会发展的必然模式,林业可持续发展是可持续发展理论在林业中的具体应用和体现。传统的林区公路网优化理论、评价指标体系和方法不能完全适应林业可持续发展的需求,因此构建一套完整的适应林业可持续发展的林区公路网评价指标体系,对于深入研究林区公路网的优化和评价问题具有重要的意义和应用价值。本文基于第三章构建的评价指标体系,将支持向量机和蚁群算法分别应用到林区公路网的评价和优化领域,研究结果表明这两种方法完全可以应用于林区公路网的评价与优化问题中,且性能较常用的方法好,结果合理可行。
本文的主要研究内容和研究结果如下：
(1)借鉴国内外公路网评价指标体系构建的方法和部分研究成果,使用频数分析法、专家咨询法和层次分析法,筛选出林区公路网评价指标,计算每个指标的权重,最终构建了完整的基于林业可持续发展理论的林区公路网评价指标体系。
(2)在对比分析几种常用支持向量机分类算法和核函数优劣的基础上,采用对多分类算法和径向基核函数,基于本文构建的林区公路网评价指标体系,建立了一种基于支持向量机的林区公路网评价模型,使用libsvm软件包完成核函数参数的选定、样本的训练和测试,分别以广州市公益林公路网、岳阳林纸商品林公路网为实例,进行了实证研究,同时对比了BP神经网络、模糊综合评价和k-近邻分类算法的分类性能,结果表明支持向量机模型具有更好的分类性能。
(3)采用最大最小蚁群算法,设计了信息素浓度的更新策略、启发因子的取值方法和蚂蚁的搜索迭代步骤,基于本文构建的林区公路网评价指标体系,建立了一种基于最大最小蚁群算法的林区公路网优化模型,并分别以广州市公益林公路网、岳阳林纸商品林公路网为研究实例进行实证研究,对比了基本蚁群算法和遗传算法的优化性能,结果表明最大最小蚁群算法模型具有更好的优化性能。优化过程采用评价、优化、再评价的方式,对比分析优化前后的公路网评价结果,验证了优化模型的有效性。
Sustainable development is the necessary develop mode of the human society, forestry sustainable development is a specific application and reflective of the sustainable development theory. The traditional forest road network optimization theory, the evaluation index system and the method can't adapt to the need of sustainable development of forest region, therefore, it has important significance and application value for the optimization and the evaluation research on forest road network to construct a complete evaluation index system of forest road network which can adapt to the need of sustainable development. This dissertation put support vector machine and ant colony algorithm into the evaluation on the forest road network and optimization baesd on the evaluation index system constructed in the third chapter. Research results show that these two methods can completely be used in the evaluation and optimization problems on the forest road network, and the performance is better than the methods commonly used, the result is reasonable and feasible.
The main research contents of this dissertation are:
(1) Referenced the construction method and research results of evaluation index system of the forest road network at home and abroad, the evaluation index of sustainable development of forest road network were screened by frequency analysis, delphi method and analytic hierarchy process, each of the index weight was calculated, and finally the complete evaluation index system of forest road network was constructed based on tne sustainable development theory.
(2) On the basis of comparison and analysis the quality of several common support vector machine (SVM) classification algorithm and kernel function, a kind of forest road network evaluation model was established based on support vector machine, used the One-Against-The-Rest classification algorithm and radial basis kernel function based on the evaluation index system of forest road network. The libsvm package was used in the kernel function parameter selection, sample training and testing. The public welfare forest road network in Guangzhou and commercial forest road network from YueYang forest and paper Co., LTD were used for empirical research respectively, compared to the BP neural network, fuzzy comprehensive evaluation and k nearest neighbor classification algorithm, the results show that
本文的主要研究内容和研究结果如下：
(1)借鉴国内外公路网评价指标体系构建的方法和部分研究成果,使用频数分析法、专家咨询法和层次分析法,筛选出林区公路网评价指标,计算每个指标的权重,最终构建了完整的基于林业可持续发展理论的林区公路网评价指标体系。
(2)在对比分析几种常用支持向量机分类算法和核函数优劣的基础上,采用对多分类算法和径向基核函数,基于本文构建的林区公路网评价指标体系,建立了一种基于支持向量机的林区公路网评价模型,使用libsvm软件包完成核函数参数的选定、样本的训练和测试,分别以广州市公益林公路网、岳阳林纸商品林公路网为实例,进行了实证研究,同时对比了BP神经网络、模糊综合评价和k-近邻分类算法的分类性能,结果表明支持向量机模型具有更好的分类性能。
(3)采用最大最小蚁群算法,设计了信息素浓度的更新策略、启发因子的取值方法和蚂蚁的搜索迭代步骤,基于本文构建的林区公路网评价指标体系,建立了一种基于最大最小蚁群算法的林区公路网优化模型,并分别以广州市公益林公路网、岳阳林纸商品林公路网为研究实例进行实证研究,对比了基本蚁群算法和遗传算法的优化性能,结果表明最大最小蚁群算法模型具有更好的优化性能。优化过程采用评价、优化、再评价的方式,对比分析优化前后的公路网评价结果,验证了优化模型的有效性。
Sustainable development is the necessary develop mode of the human society, forestry sustainable development is a specific application and reflective of the sustainable development theory. The traditional forest road network optimization theory, the evaluation index system and the method can't adapt to the need of sustainable development of forest region, therefore, it has important significance and application value for the optimization and the evaluation research on forest road network to construct a complete evaluation index system of forest road network which can adapt to the need of sustainable development. This dissertation put support vector machine and ant colony algorithm into the evaluation on the forest road network and optimization baesd on the evaluation index system constructed in the third chapter. Research results show that these two methods can completely be used in the evaluation and optimization problems on the forest road network, and the performance is better than the methods commonly used, the result is reasonable and feasible.
The main research contents of this dissertation are:
(1) Referenced the construction method and research results of evaluation index system of the forest road network at home and abroad, the evaluation index of sustainable development of forest road network were screened by frequency analysis, delphi method and analytic hierarchy process, each of the index weight was calculated, and finally the complete evaluation index system of forest road network was constructed based on tne sustainable development theory.
(2) On the basis of comparison and analysis the quality of several common support vector machine (SVM) classification algorithm and kernel function, a kind of forest road network evaluation model was established based on support vector machine, used the One-Against-The-Rest classification algorithm and radial basis kernel function based on the evaluation index system of forest road network. The libsvm package was used in the kernel function parameter selection, sample training and testing. The public welfare forest road network in Guangzhou and commercial forest road network from YueYang forest and paper Co., LTD were used for empirical research respectively, compared to the BP neural network, fuzzy comprehensive evaluation and k nearest neighbor classification algorithm, the results show that
引文
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