基于改进蚁群算法的机房排课系统设计与实现

针对机房排课中存在利用率不高的问题,将蚁群算法应用到机房排课问题中,建立了机房排课问题的数学模型,研究蚁群算法对机房排课问题的具体解法.实践结果表明,这种改进的蚁群算法提高了排课质量和效率.     

一种改进的混合排课算法研究与实现

针对目前常用的排课算法中存在的不足,分析了基于遗传算法的单一排课算法存在影响排课因素多、难以进行最优组合及排课效率低等问题,提出了一种基于优化的遗传算法和贪婪算法组合的混合排课算法。该算法首先采用改进的遗传算法对教学时间片进行安排,然后再采用贪婪算法进行教学场地安排,该算法的创新点在于简化了影响排课结果的因素,将一个复杂的过程分解为两个阶段来实现,保证了排课结果的合理性、提高了自动排课的效率、有利于后期部分课程的手动调整。     

基于量子进化算法的高校排课方法设计

排课既是高校教学管理中一项重要而复杂的工作,又是一个多目标约束组合优化问题,为此使用量子进化算法来求解高校排课问题。首先研究了量子进化算法的关键技术,然后根据量子进化算法求解优化问题的思路设计了高校排课算法。通过实验表明,本文方法能够根据开课任务自动完成排课过程,排课的成功率和效率都比较好。     

基于VC++和SQL Server的排课系统

本文尝试性地提出了一种大学排课算法,并利用VC++和SQL Server开发工具开发而成小型排课系统。排课问题的研究主要是解决各种教学资源如教室、教师的合理有效利用问题,避免教师、班级在上课时间、地点上的冲突,使排课时间分配均匀。在算法设计中为排课操作设置了不同的优先级,逐级排课,有效地降低了排课时间冲突的几率。另外,系统为教师和教室分别设计了排课级别,从而能够避免教学资源过于集中地使用,实现均衡化利用。     

一种改进蚁群算法在排课中的应用研究

为了合理安排课表、有效地利用教学资源、提高教学质量。根据排课问题的约束条件．将排课问题转化为二分图匹配问题,从而提出一种优化的蚁群算法解决排课问题。实践结果表明,这种优化的蚁群算法提高了排课质量和效率。     

一种智能化排课算法的设计

针对遗传算法容易陷入早熟收敛,无法自适应具有NP难度的多种约束条件下的排课问题,提出了一种基于量子进化算法的智能化排课算法。对排课的冲突要素和约束条件进行定义,构建了排课模型。引入量子进化算法,基于班级、时间元集合的向量矩阵构造了量子染色体,基于软约束条件的最优解设计了适应度函数,基于量子进化算法的计算框架设计了排课算法。实验表明文中算法具有智能性,能够根据开课任务自动生成排课方案,而且排课的质量和效率都优于文中的对比算法。     

基于PageRank算法的改进物流节点选址模型研究

文中针对物流网络设计中的关键物流配送节点选址问题传统算法的局限性,结合PageRank算法和贪心算法,设计了一种考虑交通拥堵情况和节点利用率的连续型多物流节点选址算法。算法将各交通小区货运需求量通过PageRank算法进行推荐排序,然后使用贪心算法和聚类算法不断迭代,获得覆盖所有需求同时满足利用率的物流节点。同时基于南京市仙林区物流数据展开实验。实验结果显示本文提出的算法相比较传统的多元节点算法,在物流节点个数方面,数量有明显减少;在服务交通小区覆盖率方面,本文提出算法覆盖率略低于多元节点法,但覆盖率均超过90%,即该算法覆盖范围利用率均能达到平均水平。综合考虑物流节点的建设及运营成本,可得出如下结论:文中提出的基于PageRank算法的连续型多物流节点覆盖算法较传统的节点选择算法高效。     

基于贪心蚁群算法的生鲜配送全局路径规划

人们生活质量的提高,使得生鲜产品的种类和需求增加,而生鲜配送过程中存在的物流成本高、易腐烂变质、车辆载重率低、送达时效性低等问题,因此传统的配送方式已经难以适应生鲜物流的需求。通过研究贪心算法和蚁群算法,将贪心算法引入到蚁群算法中来提高蚁群算法的局部搜索能力,从而来设计出贪心蚁群算法,利用贪心蚁群算法得出优化后的车辆配送路线。通过将贪心蚁群算法、贪心算法和蚁群算法对比后,结果表明贪心蚁群算法能够在原有基础上优化路径,达到生鲜配送路径最短、有效降低成本的目标。     

基于贪心遗传算法求解0-1背包问题

在解决0-1背包问题中,将贪心算法和遗传算法相结合,提出了贪心遗传算法。通过算法构造出更优的新算子,与原有算子相比,既加快了算法的收敛速度,又克服了传统方法容易陷入局部最优的特点,提高了搜索效率。通过计算机仿真试验结果表明,贪心遗传算法相比普通的遗传算法具有更好的近似解,充分证明了贪心遗传算法来求解背包问题的有效性和实用性。     

基于学分制的排课冲突检测优化算法研究

排课是教务管理工作的重要组成部分,随着学分制改革的推进,其中排课冲突检测受约束条件越来越多,越来越复杂。为提高排课冲突检测过程的运行效率,本文在排课算法性能研究比较的基础上,提出了采用位运算的排课冲突检优化测算法,设计实现了与算法匹配的数据结构及代码。该算法在云南农业大学学分制排课系统中得到高效运行。     

基于混合算法的智能排课系统

传统采用单一排课算法设计的排课系统,编排出的课程表总是与期望结果相差太大。为了使课表编排结果能满足教学要求,在此对排课约束条件进行了详细分析,采用基于遗传算法和贪婪算法的混合算法进行排课系统设计,将排课分为时间安排和地点安排2部分进行,时间安排采用遗传算法设计,地点安排采用贪婪算法设计,时间安排和地点安排过程可以人工干预,编排的课程表基本符合教务管理需求。混合算法设计的智能排课系统编排的课程表更加科学化、合理化和人性化。     

基于贪婪算法的多媒体教室排课算法设计

课程安排是高校教务管理工作中的点和难点,尤其是多媒体教室因为设备类型不统一、操作方式有差异等原因,为课程合理化安排增加了极大的困难.为了使课表编排的结果更加人性化,文中对多媒体教室排课约束条件进行详细分析,设计了基于贪婪算法的多媒体教室排课算法,使用该算法设计的多媒体教室排课系统安排出的课程表更加科学化、合理化和人性化.     

Binary monkey algorithm for approximate packing non-congruent circles in a rectangular container

A Packing problem consists in the best arrangement of several objects inside a bounded area named as the container. This arrangement must fulfill with technological constraints, for example, objects should not be overlapping. Some packing models for circular objects are typically formulated as non-convex optimization problems; where the continuous variables are the coordinates of the objects, so they are limited to not finding optimal solutions. Due to the combinatorial nature in the arrangement of such objects, heuristic methods are being used extensively which combine methods of global search and methods of local exhaustive search of local minima or their approximations. In this paper, we will address the packing problem for non-congruent (different size) circles with the binary version of the monkey algorithm which incorporates a cooperation process and a greedy strategy. We use a rectangular grid for covering the container. Every node in the grid represent potential positions for a circle. In this sense, binary monkey algorithm for the knapsack problem, can be used to solve de 0–1 approximate packing problem for non-congruet circles. The binary monkey problem uses two additional processes of the original monkey algorithm, these two processes are a greedy process and a cooperation processes.

     

Auction-Based Throughput Maximization in Cognitive Radio Networks Under Interference Constraint

Cognitive radio is an emerging technique to improve the utilization of radio frequency spectrum in wireless communication networks. That is, spectrum efficiency can be increased significantly by giving opportunistic access of the frequency bands to a group of cognitive users to whom the band has not been licensed. In this paper, as a cross layer application (MAC and physical layers) of graph theory, we consider the problem of throughput maximization of spectrum allocation in cognitive radio networks under interference constraint. We propose a novel auction-based channel allocation mechanism which tries to maximize both total and primary users’ utilities while satisfying signal to interference ratio constraint on primary receivers so that transmitted packets will be successfully received, without controlling secondary user powers. For comparison we discuss a greedy algorithm as well, however, one that does not handle interference issue. In order to compare results of proposed and greedy algorithms, we propose net throughput by taking into account outage probability of primary receiver. Simulation results show that exposing higher SINR (outage) threshold not only decreases total gain and primary users’ utilities but also worsens channel distribution performance. On the other hand adding auction mechanism significantly increases total gain throughput and primary user’ s utility. Particularly, up to SINR threshold values of 20 dBs, auction provides outstanding performance and proposed algorithm has total throughput results close to those of the greedy one even though no interference constraint is applied in the greedy algorithm. Another noticeable point of simulation results is crossover of net throughputs of proposed and greedy algorithms at a SINR threshold level after which results of ABSA-UNIC and NASA-UNIC are much better. This clearly shows superiority of proposed mechanism.     

Performance evaluation. Of the beacon period contraction algorithm in UWB MBOA MAC

Beacon period (BP) is an overhead in UWB MBOA MAC, since no data frame is allowed during a BP. In this letter, we model the current BP contraction scheme in MBOA spec as a greedy distance-2 graph coloring algorithm, and formulate the BP contraction problem as a 0-1 programming problem. The theoretical numerical results show that the current BP contraction algorithm has good performance. Furthermore, we study the impact of device joining/leaving order on the performance of the algorithm. Simulation results demonstrate that the impact is not significant and the performance of the algorithm is stable with varied device ordering. Therefore, we conclude that there is no much room for any further significant improvement over the current BP contraction algorithm in MBOA spec.     

加油站选择问题的贪心解法

贪心算法以其简单、直观、有效而受到人们的重视，特别是对于具有最优子结构和贪心选择性质的一类实际问题．它一般可以通过一系列局部最优选择来获得整体最优解。本文首先对加油站选择问题进行了分析，并给出了该类问题的贪心解法，同时对所提出算法的时间复杂度进行了分析。实验结果验证了所提出方法的有效性。