基于免疫遗传算法的排课方法的研究

在排课问题中引入免疫遗传算法,即基于免疫算法和遗传算法的优化算法,该算法具有可防止未成熟收敛和保证种群的多样性等优点。使用此算法搜索最优解时,可防止陷入局部寻优情况的出现。针对排课问题的复杂性,给出了排课问题的数学模型并提出基于免疫遗传算法的解决方案。结果表明,该算法能比较有效地解决排课问题。     

基于三维免疫遗传算法的高校排课问题研究

深入分析了高校排课问题,建立了其数学优化模型,构建了它的基本求解框架。针对高校排课问题的特点,引入遗传算法来加以解决,设计了多种改进方案,包括:三维编码方案、初始种群生成方案、适应度函数设计方案、免疫策略、自适应交叉概率和自适应变异概率设计方案。仿真结果表明该算法能够满足高校排课问题的多重约束条件,能更有效地解决高校排课问题。     

基于新型免疫遗传算法的高校排课仿真研究

研究高校排课问题,关系到高校教学质量的提高和教学资源的充分利用,随着高校教学的深入改革和高校的扩招,优化排课的高效解决就变得更加迫切。通过深入分析高校排课问题,建立了排课问题的数学优化模型,构建了它的基本求解过程。针对高校排课问题的特点,引入遗传算法来加以解决,设计了多种改进方案,包括:新的二进制编码方案、初始种群生成方案、适应度函数设计方案、免疫策略、自适应交叉概率和自适应变异概率设计方案。仿真结果表明新型算法能满足高校排课问题的多重约束条件,能更有效地解决高校排课问题。     

用量子遗传算法求解大学排课问题

作为典型的NP完全问题,大学排课问题在教务管理系统中非常重要。该文通过对大学排课问题的数学模型的分析,运用量子遗传算法进行求解。实验结果表明,利用量子遗传算法求解大学排课问题要优于使用遗传算法。     

改进遗传算法在排课问题中的应用研究

深入分析了排课问题的内在实质,抽象出求解排课问题的数学模型。在此基础上详细地阐述了如何将遗传算法运用到排课问题中,同时针对传统的遗传算法进行适当的改进。对比试验证明改进的算法能够提高智能排课的效率。     

基于约束满足和遗传算法的排课算法

针对高校排课过程中存在诸多资源约束因素的问题,提出一种将遗传算法与约束满足算法相结合的排课算法,由约束满足算法确定排课任务的优先次序,遗传算法解决单个排课任务时间片分配的优化问题。算法中单个排课任务的局部最优解具有全局最优性。实验结果表明,该算法能够改进算法性能,提高排课效率。     

高校智能排课系统算法的研究与实现

研究高校智能排课优化问题,由于在资源的有限的条件下满足教学的有序性,使高校自动排课成为一个多约束、多目标优化问题.传统排课方法排课效率低、成功率低,导致课程之间冲突率高,无法满足现代高校教务管理要求.为了提高排课效率和排课成功率,提出一种自适应遗传算法的智能排课系统.首先根据教师、学生、教室、课程和课程时间段要求建立一个多约束条件的高校排课数学模型,采用随机可行排课法操作产生可行排课方案,然后利用遗传算法在可行方案中寻找最优排课方案.仿真结果表明,相对于传统排课方法,自适应遗传算法不仅提高了排课效率,而且提高排课的成功率,有效降低课程之间冲突率,并能够解决高校排课难题.     

基于空间模型和遗传算法的高校排课系统

如何实现具有人性化设计、能满足多约束条件的高效排课系统是高校教务工作的难点问题. 针对某高校排课问题, 提出基于空间模型的高校排课算法. 首先对比分析现行各排课算法的优劣性, 然后结合该校实际排课需要, 构建排课问题空间模型和约束模型以降低排课问题复杂度, 并在此基础上, 对遗传算法进行优化, 设计了基于遗传算法的排课算法, 提出了排课问题的解决方案. 实验结果表明, 该排课系统较好的解决了该校的排课问题, 为其他类似问题的解决提供了可借鉴经验.     

基于遗传算法的排课问题研究

排课问题是一个有约束、多目标的组合优化问题,同时也是一个NP-hard问题。因此,该文选用将遗传算法引入排课问题中,首先对排课问题进行了描述,在此基础上提出了一种基于遗传算法的排课算法,并对其进行了仿真实验,最后较快的找到了问题的最优解或次优解。     

基于遗传禁忌算法结合解决排课问题

排课问题是一典型NP-Hard问题，通常可以使用遗传算法进行解决，把遗传算法与局部搜索方法禁忌算法有机结合起来，是改进遗传算法性能的一个卓有成效的方法。使用遗传禁忌算法解决排课问题，并且通过改变个体适应度的计算方法，避免了排课中课表的两极分化现象。通过实验，该方法可以取得较好的排课结果。     

基于混沌免疫遗传优化的粒子滤波重采样

针对粒子滤波算法中粒子多样性退化问题,提出一种利用混沌免疫遗传算法进行重采样的粒子滤波改进方法。该算法利用混沌的局部寻优加快搜索速度;通过免疫原理的浓度计算及加入新的混沌序列来增加种群的多样性,提高全局搜索能力,避免早熟收敛。实验结果表明该方法与基于免疫遗传算法的重采样相比较,具有更好的全局寻优能力和更快的收敛速度。     

一种基于遗传算法在编排课程表中的应用

课程表排课安排和管理是每个学校教务活动中非常重要的工作,它依靠计算机来完成复杂的排课部分,避免了手工排课产生的老师上课时间冲突和教室冲突。该文运用遗传算法的全局寻优对自动排课系统的设计构思和实现过程进行了研究,并利用遗传算法对问题进行求解。在演化过程中采用一种新的遗传策略,加速了群体的收敛速度。并得到了一个解决适合学校要求的课程表模型的好的算法。     

A novel and effective image encryption algorithm based on chaos and DNA encoding

In this paper, we proposed a novel and effective image encryption algorithm based on Chaos and DNA encoding rules. Piecewise Linear Chaotic Map (PWLCM) and Logistic Map are applied to generate all parameters the presented algorithm needs and DNA encoding technology functions as an auxiliary tool. The proposed algorithm consists of these parts: firstly, use PWLCM to produce a key image, whose pixels are generated by Chaos; Secondly, encode the plain image and the key image with DNA rules by rows respectively and different rows are encoded according to various rules decided by logistic map; After that, employ encoded key image to conduct DNA operations with the encoded plain image row by row to obtain an intermediate image and the specific operation executed every row is chosen by logistic map; Then, decode the intermediate image as the plain image of next step. Finally, repeat steps above by columns again to get the ultimate cipher image. The experiment results and analysis indicate that the proposed algorithm is capable of withstanding typical attacks and has good character of security.     

求解加权圆集布局问题的快速启发式搜索算法

加权圆集布局问题是基于性能驱动的一类布局问题,由于其NP-hard属性,难以在多项式时间内求解,提出一种快速启发式搜索算法。权矩阵的行向量1范数作为首次赌轮选择圆的启发信息,依次以权矩阵的当前行（其行号等于当前选择圆的序号）元素作为下次赌轮选择的启发信息,利用图形学理论给出低计算复杂度的定位规则,进而基于该定序定位规则提出一种启发式搜索算法,以求得该问题的最优解。数值实验表明,该算法的性能优于已有算法。     

Tile-Packing Tomography Is \mathbb{NP}-hard

Discrete tomography deals with reconstructing finite spatial objects from their projections. The objects we study in this paper are called tilings or tile-packings, and they consist of a number of disjoint copies of a fixed tile, where a tile is defined as a connected set of grid points. A row projection specifies how many grid points are covered by tiles in a given row; column projections are defined analogously. For a fixed tile, is it possible to reconstruct its tilings from their projections in polynomial time? It is known that the answer to this question is affirmative if the tile is a bar (its width or height is 1), while for some other types of tiles $\mathbb {NP}$ -hardness results have been shown in the literature. In this paper we present a complete solution to this question by showing that the problem remains $\mathbb {NP}$ -hard for all tiles other than bars.     

基于交叉扫描的LTI在超声层析中的应用

目的研究线性插值射线追踪的新方法,提高追踪精度。方法在基本的线性插值射线追踪的基础上,对每一网格单元边界的周围结点进行上下左右交叉扫描。结果和结论本算法,在保留原有算法的优点的基础上,解决了基本算法中当发射点所在列有低速区时,与发射点同列的接收点的走时路径无法绕开该区域的缺点,提高了算法的精度。     

基于二维检索的投影矩阵算法

传统的投影矩阵算法复杂度较高,计算效率低。为此,提出一种基于二维检索的投影矩阵算法。采用类似于矩阵中各元素位置的表示方式,分别用行和列2个维度定位一个投影矩阵的元素,行和列计算较为简单,能够同时进行,从而提高计算效率。运用GATE仿真软件进行图像重建实验,结果表明,与经典的Siddon算法相比,该算法的运算时间可提高6倍以上,并且能够保证重建图像的质量。     

基于相对Hamming距离的Web聚类算法

针对Web使用挖掘中聚类结果准确性不高的问题,提出了一种改进的基于相对Hamming距离和类不一致度的聚类算法。该算法首先以Web站点的URL为行、以UserID为列建立关联矩阵,元素值为用户的访问次数;然后,对所建立关联矩阵的列向量或行向量进行相似性度量,获得相似客户群体或相关页面。实验表明,该算法具有较高的准确性。     

Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported that hyper heuristics are well applied in combinatorial optimisation problems. As a classic combinatorial optimisation problem, the row layout problem has not been publicly reported on applying hyper heuristics to its various sub-problems. To fill this gap, this study proposes a parallel hyper-heuristic approach based on reinforcement learning for corridor allocation problems and parallel row ordering problems. For the proposed algorithm, an outer layer parallel computing framework was constructed based on the encoding of the problem. The simulated annealing, tabu search, and variable neighbourhood algorithms were used in the algorithm as low-level heuristic operations, and Q-learning in reinforcement learning was used as a high-level strategy. A state space containing sequences and fitness values was designed. The algorithm performance was then evaluated for benchmark instances of the corridor allocation problem (37 groups) and parallel row ordering problem (80 groups). The results showed that, in most cases, the proposed algorithm provided a better solution than the best-known solutions in the literature. Finally, the meta-heuristic algorithm applied to three low-level heuristic operations is taken as three independent algorithms and compared with the proposed hyper-heuristic algorithm on four groups of parallel row ordering problem instances. The effectiveness of Q-learning in selection is illustrated by analysing the comparison results of the four algorithms and the number of calls of the three low-level heuristic operations in the proposed method.     

Solving connected row convex constraints by variable elimination

We propose an algorithm for the class of connected row convex constraints. In this algorithm, we introduce a novel variable elimination method to solve the constraints. This method is simple and able to make use of the sparsity of the problem instances. One of its key operations is the composition of two constraints. We have identified several nice properties of connected row convex constraints. Those properties enable the development of a fast composition algorithm whose complexity is linear to the size of the variable domains. Compared with the existing work including randomized algorithms, the new algorithm has favorable worst case time and working space complexity. Experimental results also show a significant performance margin over the existing consistency based algorithms.