This paper first analyzes the significance of applying mixed teaching to the“Python Language Programming”course,briefly describes the current state of teaching in“Python Language Programming,”and discusses strateg...This paper first analyzes the significance of applying mixed teaching to the“Python Language Programming”course,briefly describes the current state of teaching in“Python Language Programming,”and discusses strategies for reforming mixed teaching approaches.The goal is to provide a reference for the innovative development of teaching the“Python Language Programming”course.展开更多
Graph partitioning problem is a classical NP-hard problem.The improvement of graph partitioning results by vertex migration is an important class of methods for graph partitioning.The goal of graph partitioning is get...Graph partitioning problem is a classical NP-hard problem.The improvement of graph partitioning results by vertex migration is an important class of methods for graph partitioning.The goal of graph partitioning is getting a partition with the least number of cut edges,while also satisfying the capacity limit of the partition.In this paper,an optimization model for vertex migration is proposed,considering the influence between neighboring vertices,so that the objective function value of the model is exactly equal to the amount of cut edge variation.The model is converted into a mixed 0-1 linear programming by introducing variables.Then,a heuristic iterative algorithm is designed,in which the mixed 0-1 linear programming model is transformed into a series of small-scale models that contain less integer variables.In the experiment,the method in this paper is simulated and compared with balanced label propagation methods and their related methods.The improvement effect of these methods based on three different initialization methods is analyzed.Extensive numerical experiments on five commonly used datasets validate the effectiveness and efficiency of the proposed method.展开更多
集电系统拓扑优化是大型海上风电场规划建设的核心问题,本质上是一个涉及多约束、多目标的复杂混合整数优化问题。针对该问题,提出了一种基于大语言模型(large language model,LLM)辅助的大型海上风电场集电系统拓扑优化方法。首先,基...集电系统拓扑优化是大型海上风电场规划建设的核心问题,本质上是一个涉及多约束、多目标的复杂混合整数优化问题。针对该问题,提出了一种基于大语言模型(large language model,LLM)辅助的大型海上风电场集电系统拓扑优化方法。首先,基于大语言模型辅助对风电机组群进行聚类,通过链式提示法使LLM理解优化目标,并利用LLM将大型海上风电场分割为若干小型区域,以降低优化问题维度,提升求解速度和质量。然后,构建集电系统拓扑优化模型,基于混合整数线性规划求解器,获得海上风电场的最优集电系统拓扑设计方案。最后,利用1个含有75台风电机组的大型海上风电场系统进行方法性能验证,仿真结果表明:与传统优化技术相比,所提方法获得的聚类风机数量更加均衡,在考虑拓扑功率损耗的同时,生成的拓扑方案经济性最优。LLM在集电系统拓扑辅助优化中具有较高的有效性,为海上风电场集电系统拓扑设计优化提供了一种新思路。展开更多
摘要This paper first analyzes the significance of applying mixed teaching to the“Python Language Programming”course,briefly describes the current state of teaching in“Python Language Programming,”and discusses strategies for reforming mixed teaching approaches.The goal is to provide a reference for the innovative development of teaching the“Python Language Programming”course.
基金supported by the National Key Research and Development Program of China(No.2022YFA1003900).
摘要Graph partitioning problem is a classical NP-hard problem.The improvement of graph partitioning results by vertex migration is an important class of methods for graph partitioning.The goal of graph partitioning is getting a partition with the least number of cut edges,while also satisfying the capacity limit of the partition.In this paper,an optimization model for vertex migration is proposed,considering the influence between neighboring vertices,so that the objective function value of the model is exactly equal to the amount of cut edge variation.The model is converted into a mixed 0-1 linear programming by introducing variables.Then,a heuristic iterative algorithm is designed,in which the mixed 0-1 linear programming model is transformed into a series of small-scale models that contain less integer variables.In the experiment,the method in this paper is simulated and compared with balanced label propagation methods and their related methods.The improvement effect of these methods based on three different initialization methods is analyzed.Extensive numerical experiments on five commonly used datasets validate the effectiveness and efficiency of the proposed method.
摘要集电系统拓扑优化是大型海上风电场规划建设的核心问题,本质上是一个涉及多约束、多目标的复杂混合整数优化问题。针对该问题,提出了一种基于大语言模型(large language model,LLM)辅助的大型海上风电场集电系统拓扑优化方法。首先,基于大语言模型辅助对风电机组群进行聚类,通过链式提示法使LLM理解优化目标,并利用LLM将大型海上风电场分割为若干小型区域,以降低优化问题维度,提升求解速度和质量。然后,构建集电系统拓扑优化模型,基于混合整数线性规划求解器,获得海上风电场的最优集电系统拓扑设计方案。最后,利用1个含有75台风电机组的大型海上风电场系统进行方法性能验证,仿真结果表明:与传统优化技术相比,所提方法获得的聚类风机数量更加均衡,在考虑拓扑功率损耗的同时,生成的拓扑方案经济性最优。LLM在集电系统拓扑辅助优化中具有较高的有效性,为海上风电场集电系统拓扑设计优化提供了一种新思路。