Research and Practice of Comparative Teaching of Definite Integral and Differential Element Method Based on Specialty of Information and Computing Science Features

随着科学的发展,产生出了不同的专业。每个专业由于侧重点不同,从而具有不同的特色。信息与计算科学专业是以将数学应用于解决工程问题为研究方向,具有和数学专业明显的不同。因此,对该专业的定积分与微元法的教学必须要与数学专业的形式定义有所区别,也应与其他工科专业的过于粗糙有所不同,而应该充分凸显该专业既注重形式定义的重要,也要突出内在解决实际问题的本质的特色。本文从解决实际问题的方法出发,对定积分与微元法进行比较分析,探讨适合于该专业的关于这两个定义的教学方法,旨在指出该专业所要求的教学特色,以提高专业教学质量。Different specialties are born of developing of science, and each specialty has different particular attention, and so has different features. Specialty of information and computing science lay particular emphasis on applying mathematics to solve engineering problems, so it is distinct different from the specialty of math. Therefore,the teaching of definite integral and differential element method for specialty of information and computing science students should be different to the teaching for student of specialty of math, which laying emphasis on form definition. Besides, it should be different to the teaching for student of other engineering specialty, which is over--crude. The teaching for the specialty of information and computing science students should not only emphasize definition, but also stress its essence features of the solving reality problems. In this paper, from the method of solving actual problem, definite integral and differential element method are analysed comparatively, and the teaching method for the two definitions is explored in order to point out the teaching feature and improve teaching quality

提高教学质量的分析与课堂教学比赛的模式探讨

在高校发展与社会发展的现今大背景下,提高教学质量是高校面临的重要课题。课堂教学比赛对提高教学质量与培养青年教师都起着举足轻重的作用,受到各学校的广泛青睐。但课堂比赛在显示积极因素一面的同时,也凸显了不少的问题,值得进一步的研究。本文在阐明了课堂教学比赛积极作用的同时,亦对其中隐含的问题进行深入的论述与分析,同时对所面临的问题提出了解决方法;目的在于提高教学质量,使教学质量能得到稳步地提升。新世纪广西高等教育教改工程项目(2011JGB037);广西民族大学2010年校级教学改革工程项目(3)广西民族大学2011年校级教学改革工程项目(2011XJGA08

he Lectures Evaluation System based on Degree of Difficulty

教学质量是高校的生命线,如何提高教学质量一直是高校不断探索的课程.听课、评课是提高教学质量的一种途径,但不同的评课方式对讲课者的影响是不同的,积极客观、公平的评价将会对教学起到促进的作用.在评课体系中,课程内容的难度不应该被忽视.文章以《数学分析(I)》为例,论述了以课程内容中的定义、概念、定理证明、例题等为标准而建立的基于难度系数的评价系统,旨在促使评课更加积极客观,借以促进教学水平、教学技巧的提高.How to improve the quality of teaching has been constantly exploring college projects.Evaluation is a way to improve teaching quality,but different methods on the impact of those lectures are different.The positive and objective and fair evaluation will serve to promote the role of teaching.In the evaluation system,the difficulty of the course content should not be ignored.This paper makes Mathematical Analysis（I） as an example,discusses the degree of difficulty evaluation system standard based on the definition of content,concepts,theorem proving,and examples and so on,to promote a more positive and objective observation and evaluation,in order to promote the teaching level,teaching skills improvement.新世纪广西高等教育教改工程项目(2011JGB037);广西民族大学2010年校级教学改革工程项目;广西民族大学2011年校级教学改革工程项目(2011XJGA08

Firefly Algorithm for Solving 0-1 Knapsack Problem

将贪心策略和变异策略与萤火虫算法相结合,提出一种求解0-1背包问题的贪心萤火虫算法。通过增加贪心策略和变异策略,在一定程度上能使萤火虫跳出局部极值,提高算法的性能。通过对多个实例的仿真,将该算法与其它算法如贪心遗传算法、贪心微粒群算法进行对比,对比结果表明,该算法在求解0-1背包问题上具有更强约束处理能力和快速收敛效果。 Taking advantage of the standard firefly algorithm （FA） and combining with the characteristics of the 0-1 knapsack problem, this paper designs a firefly algorithm based on 0-1 knapsack problem. After experimental simulation, we verified the firefly algorithm＇ s feasibility and effectiveness to solve 0-1 knapsack problem. Finally, after many simulation experiments, this paper analyzes the influence of various parameters on the algorithm performance, reflected the importance of selection of key pa- rameters to the algorithm optimization.中国博士后基金项目(2012M511711);广西混杂计算与集成电路设计分析重点实验室开放基金项目(2012HCI08);广西教育厅基金项目(201204LX082);广西民族大学基金项目(2011MDYB030

Artificial glowworm swarm optimization algorithm with Gauss mutation

针对基本萤火虫优化算法在求解函数全局最优值时的不足,提出了一种带高斯变异的人工萤火虫优化算法。该算法在萤火虫的移动过程中,应用了高斯变异策略,从而在一定程度上避免了算法陷入局部最优,且能获得精度更高的解。通过对六个标准测试函数进行测试,结果表明,改进后的人工萤火虫算法比基本萤火虫优化算法有更高的收敛速度、求解精度和收敛成功率。According to the basic glowworm swarm optimization algorithm problems in solving the function of global optimal value,the paper put forward an artificial glowworm swarm optimization algorithm with Gauss mutation.The algorithm used a Gauss mutation strategy in the firefly mobile process,to prevent the algorithm into a local optimum in a certain extent,and obtained a more accurate solution.Finally,the test results of six standard test functions show that,the improved algorithm has higher convergence speed,solution precision and convergence rate of success than the basic GSO algorithm.中国博士后基金资助项目(2012M511711);广西教育厅资助项目(201204LX082);广西民族大学资助项目(2011MDYB030

Artificial glowworm swarm optimization algorithm based on biological predator-prey behavior

针对基本萤火虫优化(GSO)算法在求解函数全局最优值时,存在着易陷入局部最优、收敛速度慢和求解精度低等问题,提出了1种基于生物捕食-被捕食(Predator-Prey)行为的双种群GSO算法(GSOPP)。该算法通过引入种群间的追逐与逃跑以及变异等策略加快了收敛速度,且能获得精度更高的解。最后,通过对8个标准测试函数进行测试,结果表明,改进后的GSOPP算法比基本GSO算法有更优的性能。According to the basic glowworm swarm optimization （GSO） algorithm in solving the function of global optimal value existing some problems, such as easy to fall into local optimum, slow convergence and low precision, an artificial glowworm swarm optimization algorithm based biological predator-prey behavior （GSOPP） is proposed. The algorithm through populations chase and escape, and the mutation strategy to speed up the convergence rate, and can obtain a more accurate solution. Finally, the test results of 8 standard test functions show that, the improved GSOPP algorithm than the basic GSO algorithm has Better performance.中国博士后基金(2012M511711);广西教育厅项目(201204LX082);广西民族大学项目(2011MDYB030);广西混杂计算与集成电路设计分析重点实验室开放基金(2012HCI09

Improved firefly algorithm based on simplex method and its application in solving non-linear equation groups

萤火虫算法(FA)是一种基于群体搜索的启发式随机优化算法,其模拟自然界中萤火虫利用发光的生物学特性而表现出来的社会性行为。针对萤火虫算法存在着收敛速度慢、易陷入局部最优、求解精度低等不足,利用单纯形法局部搜索速度快和萤火虫算法全局寻优的特点,提出一种基于单纯形法的改进型萤火虫算法(SMFA)。通过对标准测试函数以及非线性方程组的实验仿真,并与其他算法进行的对比分析表明,改进后的算法在函数优化方面有较强的优势,在一定程度上有效地避免了陷入局部最优,提高了搜索的精度。The firefly algorithm （ FA） is a heuristic random optimization algorithm based on groupization. It simulates the social behavior of firefly in the natural environment represented in its biological characteristics of shining. FA has disadvantages in global searching, such as slow convergence speed, high possibility of being trapped in local optimum and low solving precision. An improved FA based on the simplex method is proposed. The proposed method combines the characteristics of speedy local search of simplex method with the global optimization of firefly algorithm. The simplex method modifies the firefly, which is located at poor positions through its reflection, expansion and compression operation. However, it improves the diversity of individuals and avoids falling into local optimum and improves the precision of the algorithm. The results showed that through simulations of standard benchmark functions and nonlinear functions and contrasted with other algorithms, the improved algorithm has a strong advantage in function optimization. It also avoids trapping in local optimum and improves the calculation accuracy to a certain extent.国家自然科学基金资助项目(21466008);广西民族大学科研资助项目(2014MDYB030