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论文中文题名:

 柯西自适应回溯搜索的耦合算法及应用研究    

姓名:

 赵福媛    

学号:

 19201103009    

保密级别:

 公开    

论文语种:

 chi    

学科代码:

 070104    

学科名称:

 理学 - 数学 - 应用数学    

学生类型:

 硕士    

学位级别:

 理学硕士    

学位年度:

 2022    

培养单位:

 西安科技大学    

院系:

 理学院    

专业:

 数学    

研究方向:

 智能优化算法    

第一导师姓名:

 赵高长    

第一导师单位:

 西安科技大学    

第二导师姓名:

 张仲华    

论文提交日期:

 2022-06-17    

论文答辩日期:

 2022-06-09    

论文外文题名:

 Coupling algorithm of Cauchy adaptive backtracking search and its application    

论文中文关键词:

 回溯搜索优化算法 ; 柯西种群生成策略 ; 自适应变异因子策略 ; 决策理论粗糙集 ; 肿瘤基因表达数据    

论文外文关键词:

 Backtracking search optimization algorithm ; Cauchy population generation strategy ; Adaptive mutation factor strategy ; Decision-theoretic rough sets ; Tumor gene expression data    

论文中文摘要:

随着计算机科学和互联网技术的飞速发展,优化算法已经成为热点研究领域之一。回溯搜索优化算法是基于种群的新型元启发式优化算法,该算法结构简单、寻优能力强、计算效率高,自提出以来被广泛应用于多个领域。然而该算法存在历史信息和当代信息趋于相同时容易陷入早熟状态,变异控制参数随机性较强,局部开采能力弱等问题。本文提出一种基于集体智慧的改进方法,分别与最小二乘支持向量机和支持向量机结合构建两种新的集成预测模型,用于工程预测和肿瘤分类问题。

首先针对回溯搜索优化算法易早熟、随机性较强和局部开采能力弱等问题,采用柯西种群生成策略增加历史种群多样性,结合自适应变异因子策略调节变异尺度系数的优化机理,提出一种基于集体智慧的柯西自适应回溯搜索算法,运用改进后的柯西自适应回溯搜索算法优化最小二乘支持向量机构建新的集成预测回归模型。选取10个UCI数据集进行数值实验,结果表明所提模型在种群规模为80时回归预测性能最优,并与其他模型相比较,该模型具有较高的预测精度和较快的计算速度。

其次针对肿瘤基因表达数据的特征基因选取和分类模型优化问题,采用适应大规模冗余和不确定性数据的模糊邻域三支决策模型,结合广义粗糙集模型的单参数决策理论粗糙集策略,提出一种基于模糊邻域三支决策粗糙集的属性约简算法,运用改进后的柯西自适应回溯搜索优化算法优化支持向量机构建新的集成预测分类模型。选取7个肿瘤基因表达数据进行属性约简,并对约简后的肿瘤基因表达数据进行数值实验,结果表明所提模型与其他模型相比较,该模型具有良好的鲁棒性和更优的分类性能。

本文对回溯搜索优化算法的改进策略展开深入研究,不仅平衡了算法的全局勘探和局部开采能力,而且为其在实际工程领域的优化求解和生物医学领域的临床诊断提供理论依据。

论文外文摘要:

With the rapid development of computer science and Internet technology, optimization algorithm has become one of the most popular research fields. Backtracking search optimization algorithm is a new meta-heuristic optimization algorithm based on population. The algorithm has simple structure, strong optimization ability and high computational efficiency, and has been widely used in many fields since it was proposed. However, the algorithm still has some problems, such as the tendency of historical information and contemporary information to fall into premature state, the randomness of variation control parameters is strong, and the local mining capacity is weak. In this paper, an improved method based on collective intelligence was proposed to construct two new integrated prediction models, which were combined with least square support vector machine and support vector machine respectively, for engineering prediction and tumor classification.

Firstly, aiming at the problems of easy to premature, strong randomness and weak local mining capacity of the backtracking search optimization algorithm, the Cauchy population generation strategy was used to increase the diversity of historical populations, and the adaptive mutation factor strategy was used to adjust the optimization mechanism of the variation scale coefficient. The Cauchy adaptive backtracking search algorithm based on collective intelligence, a new ensemble predictive regression model was constructed by using the improved Cauchy adaptive backtracking search algorithm to optimize the least squares support vector machine. Ten UCI datasets were selected for numerical experiments. The results show that the proposed model has the best regression prediction performance when the population size is 80. Compared with other models, the proposed model has higher prediction accuracy and faster calculation speed.

Secondly, aiming at the problem of feature gene selection and classification model optimization of tumor gene expression data, a fuzzy neighborhood three-way decision model adapted to large-scale redundant and uncertain data, and a single-parameter decision theory rough set strategy of generalized rough set model were adopted. An attribute reduction algorithm based on the fuzzy neighborhood three-way decision rough set was proposed, and used the improved Cauchy adaptive backtracking search optimization algorithm to optimize the support vector machine to build a new ensemble prediction classification model. Seven tumor gene expression data were selected for attribute reduction, and numerical experiments were conducted on the reduced tumor gene expression data. The results show that the proposed model has good robustness and better classification performance compared with other models.

In this paper, the improvement strategy of the backtracking search optimization algorithm was deeply studied, which not only balances the global exploration and local mining ability of the algorithm, but also provides a theoretical basis for its optimization solution in the practical engineering field and clinical diagnosis in the biomedical field.

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中图分类号:

 TP301.6    

开放日期:

 2022-06-23    

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