Using particle swarm optimization to solve effectively the school timetabling problem

A new hybrid adaptive algorithm based on particle swarm optimization (PSO) is designed, developed and applied to the high school timetabling problem. The proposed PSO algorithm is used to create feasible and efficient timetables for high schools in Greece. Experiments with real-world data coming from different high schools have been conducted to show the efficiency of the proposed PSO algorithm. As well as that, the algorithm has been compared with four other effective techniques found in the literature to demonstrate its efficiency and superior performance. In order to have a fair comparison with these algorithms, we decided to use the exact same input instances used by these algorithms. The proposed PSO algorithm outperforms, in most cases, other existing attempts to solve the same problem as shown by experimental results.     

A hybrid particle swarm optimization based algorithm for high school timetabling problems

In this contribution a hybrid particle swarm optimization (PSO) based algorithm is applied to high school timetabling problems. The proposed PSO based algorithm is used for creating feasible and efficient high school timetables. In order to demonstrate the efficiency of the proposed PSO based algorithm, experiments with real-world input data coming from many different Greek high schools have been conducted. Computational results show that the proposed hybrid PSO based algorithm performs better than existing approaches applied to the same school timetabling input instances using the same evaluation criteria.     

Local search techniques for large high school timetabling problems

The high school timetabling problem regards the weekly scheduling for all the lectures of a high school. The problem consists in assigning lectures to periods in such a way that no teacher (or class) is involved in more than one lecture at a time, and other constraints are satisfied. The problem is NP-complete and is usually tackled using heuristic methods, This paper describes a solution algorithm (and its implementation) based on local search techniques. The algorithm alternates different techniques and different types of moves and makes use of an adaptive relaxation of the hard constraints. The implementation of the algorithm has been successfully experimented with in some large high schools with various kinds of side constraints     

天课时分配下的多阶段新高考走班制排课算法

伴随新高考改革,高中排课过程需要考虑学生的科目选择。潜在的学生上课时间冲突提高了排出可行课表的难度,排课过程中对课表的复杂要求也更难得到满足。针对这些挑战提出一种多阶段优化算法解决高中“走班制”教学课程时间表优化问题。优化侧重点从课表时段分配转为天课时分配,即对每个课程班每天的课时数目进行决策。除需要满足课时不冲突的约束条件外,主要优化目标为“课时分布均匀”“教案平齐”“同时上课”。根据问题特点设计了三种课表变换算子用于在教学班天课时分配阶段提升新设计的爬山算法的寻优能力。在三组不同难度和规模的实验数据上,多阶段优化算法以高于85%的概率排出可行课表。相较大规模真实案例,人工生成案例和中规模真实案例在目标函数上得到较为理想的优化。整体课表的教案平齐违反主要源于行政班课表。发现同时上课的设置具有指导其他目标函数优化的能力。     

Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

Predicting student failure at school has become a difficult challenge due to both the high number of factors that can affect the low performance of students and the imbalanced nature of these types of datasets. In this paper, a genetic programming algorithm and different data mining approaches are proposed for solving these problems using real data about 670 high school students from Zacatecas, Mexico. Firstly, we select the best attributes in order to resolve the problem of high dimensionality. Then, rebalancing of data and cost sensitive classification have been applied in order to resolve the problem of classifying imbalanced data. We also propose to use a genetic programming model versus different white box techniques in order to obtain both more comprehensible and accuracy classification rules. The outcomes of each approach are shown and compared in order to select the best to improve classification accuracy, specifically with regard to which students might fail.     

Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm

In this contribution we present the application of a hybrid cat swarm optimization (CSO) based algorithm for solving the school timetabling problem. This easy to use, efficient and fast algorithm is a hybrid variation of the classic CSO algorithm. Its efficiency and performance is demonstrated by conducting experiments with real-world input data. This data, collected from various high schools in Greece, has also been used as test instances by many other researchers in their publications. Results reveal that this hybrid CSO based algorithm, applied to the same school timetabling test instances using the same evaluation criteria, exhibits better performance in less computational time compared to the majority of other existing approaches, such as Genetic Algorithms (GAs), Evolutionary Algorithms (EAs), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Artificial Fish Swarm (AFS). The algorithm's main process constitutes a variation of the classic CSO algorithm, properly altered so as to be applied for solving the school timetabling problem. This process contains the main algorithmic differences of the proposed approach compared to other algorithms presented in the respective literature.     

计算机辅助课表编排技术的研究

讨论了几种实用的计算机辅助课表编排技术及在实际编排中的应用。应用分析表明，采用分批与或图搜索和分批优化的匈牙利算法相结合的方法，在计算机辅助课表编排中是行之有效的。     

An adaptive surrogate assisted differential evolutionary algorithm for high dimensional constrained problems

Differential evolution (DE) is a competitive algorithm for constrained optimization problems (COPs). In this study, in order to improve the efficiency and accuracy of the DE for high dimensional problems, an adaptive surrogate assisted DE algorithm, called ASA-DE is suggested. In the ASA, several kinds of surrogate modeling techniques are integrated. Furthermore, to avoid violate the constraints and obtain better solution simultaneously, adaptive strategies for population size and mutation are also suggested in this study. The suggested adaptive population strategy which controls the exploring and exploiting states according to whether algorithm find enough feasible solution is similar to a state switch. The mutation strategy is used to enhance the effect of state switch based on adaptive population size. Finally, the suggested ASA-DE is evaluated on the benchmark problems from congress on evolutionary computation (CEC) 2017 constrained real parameter optimization. The experimental results show the proposed algorithm is a competitive one compared to other state-of-the-art algorithms.     

用约束满足自适应神经网络和有效的启发式算法解Job-shop调度问题

提出一种用约束满足自适应神经网络结合有效的启发式算法求解Ｊｏｂ－ｓｈｏｐ调度问题．在混合算法中,自适应神经网络具有在网络运行过程中神经元的偏置和连接权值自适应取值的特性,被用来求得调度问题的可行解,启发式算法分别被用来增强神经网络的性能、获得确定排序下最优解和提高可行解的质量．仿真表明了本文提出的混合算法的快速有效性．     

An adaptive steganographic algorithm for 3D polygonal models using vertex decimation

Most 3D steganographic algorithms emphasize high data capacity, low distortion, and correct data extraction. However, their disadvantage is in the existence of the same embedding capacity for each data-embedded vertex in the 3D models. Embedding the same capacity in the vertex located on the surface with different properties may cause obvious distortion, making it difficult to achieve the initial goal of information-hiding techniques. This study proposes a new and adaptive 3D steganographic algorithm that considers the surface complexity. To increase the accuracy of the complexity estimation for each embedding vertex, the proposed algorithm adopts a vertex decimation process to determine its referencing neighbors. Thereafter, different amounts of the secret messages are embedded according to the surface properties of each vertex. This approach preserves important shape features and produces a more imperceptible result. Experimental results show that the proposed adaptive algorithm can achieve more accurate estimation results with a higher data capacity and acceptable distortion. The proposed technique is feasible in 3D steganography.     

Hybrid position–admittance realization of an adaptive output super-twisting controller for a robotic scalpel

This study proposes the design and implementation of a hybrid robust automatic controller based on the application of a high order sliding mode algorithm for a robotic scalpel prototype (RS). Two fully actuated arms with three degrees of freedom constitute the RS, one arm holds the sample and the second one has the scalpel to exert the cutting task. Each arm is attached to its corresponding cartesian robotic platform. The available measurements are the angular displacements, the linear displacement and the force vector describing the interaction between the scalpel and the biological sample. A hybrid position–admittance controller implements an output-based adaptive distributed super-twisting algorithm to mobilize the RS. A high order sliding mode observer estimates the unknown angular and linear velocities that were used in the hybrid controller. Once the end-effector of each arm reaches the desired cutting position, the designed controller switches to the admittance controller to avoid damaging the surrounding tissue. Numerical simulations show the advantages of the suggested controller in comparison with classical algorithms. The hybrid sliding mode admittance controller has been successfully evaluated on an self-constructed platform. The experimental results show a precise cut and efficient mobilization of the RS compared to other classical controllers such as proportional-differentiator, proportional-integral and first order sliding mode controllers.     

一个综合应用人工智能技术的课表专家系统

课表生成系统的主要任务是快速、准确、无遗漏地发现初始课表方案中的冲突,然后按设计的处理办法迅速生成符合教学要求的正式课表。针对现有的课表生成软件在约束条件增多、生成课表量增大的情况下处理速度非常慢的现状,综合应用了人工智能技术处理课表生成问题,取得了良好的效果,与同类软件相比,大大提高了工作效率。文中介绍了课表生成专家系统的方案框架,讨论了处理冲突的算法。     

一种快速自适应RSUSAN角点检测算法

根据图像边缘灰度的渐变性，我们重新定义SUSAN(Small Univalue Segment Assimilating Nucleus)算法中小核值相似区；并找到一种更为有效和简便的计算小核值相似区面积的方法；在此基础上提出了RSUSAN(Redefined SUSAN)角点检测算法。与经典的角点检测算法SUSAN、MIC(Minimum Intensity Change)相比，RSUSAN具有角点检测准确性高，计算简单，运算速度大为提高等优点。对于模糊、噪声大的图像本文还进一步提出了采用自适应平滑和RSUSAN相结合的方法，称为自适应RSUSAN算法。实验证明，相比较SUSAN、MIC算法而言，自适应RSUSAN算法没有显著地增加计算量，而且在对模糊、噪声大的图像进行角点检测时，虚报及漏检概率大大减少，对噪声的鲁棒性好，角点检测位置精确。     

求解矩形布局问题的自适应算法

矩形布局问题属于NP-Hard 问题,其求解算法多为启发式算法。该文侧重 于构造布局求解算法中定位函数（规则）的优化,将模拟退火算法的思想融入到遗传算法中, 提出了求解矩形布局问题的自适应算法,其利用自适应交叉、变异及接收劣质解的概率等方 法对定位函数中各参数进行优化。算法通过两种方式确定初始种群的数目,具有较强的适应 性。在算法搜索的后期,利用差异性较大的个体进行交叉操作,从而保持种群的多样性。最 后通过实例证明了该算法能够很好的应用于矩形布局问题的求解。     

Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimization

Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptive island genetic algorithm (SAIGA) for the first stage and adaptive techniques in the second stage is proposed for the use in bit-array represented topology optimization. The first stage, consisting a number of island runs each starting with a different set of random population and searching for better designs in different peaks, helps the algorithm in performing an extensive global search. After the completion of island runs the initial population for the second stage is formed from the best members of each island that provides greater variety and potential for faster improvement and is run for a predefined number of generations. In this second stage the genetic parameters and operators are dynamically adapted with the progress of optimization process in such a way as to increase the convergence rate while maintaining the diversity in population. The results obtained on several single and multiple loading case problems have been compared with other GA and non-GA-based approaches, and the efficiency and effectiveness of the proposed methodology in reaching the global optimal solution is demonstrated.     

三层物流网络选址-路径优化及混合启发式算法研究*

为同时解决转运、分配、选址和车辆路径问题,在考虑车辆载重和行驶距离约束,配送中心处理能力约束的基础上,构建了一个多产品三层物流网络选址-路径模型,以总成本最小为目标,提出一种基于贪婪随机自适应搜索算法和里程节约算法的混合启发式算法,给出了该算法的步骤和伪代码。实验结果表明该算法具有可行性,并且与其他算法比较而言,算法具有高效性。     

基于小波变换的图像自适应阈值去噪算法

针对传统小波阈值去噪算法的不足,提出了一种新的自适应阈值去噪算法.该算法引入了一个新的阈值函数,利用GGD模型对小波子带内的系数进行建模,再根据小波子带系数的局部邻域信息进行方差估计,从而得到自适应最优阈值.实验结果表明,该算法在峰值信噪比和主观视觉效果上都比传统小波阈值去噪算法具有明显改善.     

自适应路由算法优于确定性路由算法

在研究并行计算机系统的容错时。自适应路由算法是一个极为重要的研究课题．它是在网络结点出错时，算法通过可选择的路径进行路由．在每个结点具有独立的出错概率的模型下，研究Mesh网络上自适应路由算法和确定性路算法的性能．本文提出的技术使得我们能严格地推导出路由算法的成功的概率，从而能分析和比较算法的性能．研究结果表明自适应路由算法具有明显的优势：一方面确定性路算法需要全局错误信息而变得高效性，另一方面自适应路由算法对于结点出错和网络规模具有更好的健壮性而具有更高的成功概率．     

改进遗传模拟退火算法在TSP优化中的应用

针对旅行商问题(TSP)优化中,遗传算法(GA)容易陷入局部最优、模拟退火算法(SA)收敛速度慢的问题,提出一种基于改进遗传模拟退火算法(IGSAA)的TSP优化算法.首先根据优化目标建立数学模型;然后对遗传算法部分中的适应度函数、交叉变异算子进行改进,使算法能够更加有效地避免陷入局部最优;最后根据旧种群和新种群每个对应个体的进化程度提出一种改进自适应的Metropolis准则,使模拟退火算法部分的染色体跳变更具有自适应性,利于算法寻优.对不同TSP实例的实验结果表明,与其他路径优化算法优化结果相比,所提出的IGSAA算法能够对不同TSP实例优化得到更优的旅行路径.     

A new constraint handling method based on the modified Alopex-based evolutionary algorithm

In this paper, a new constraint handling method based on a modified AEA (Alopex-based evolutionary algorithm) is proposed. Combined with a new proposed ranking and selecting strategy, the algorithm gradually converges to a feasible region from a relatively feasible region. By introduction of an adaptive relaxation parameter μ, the algorithm fully takes into account different functions corresponding to different sizes of feasible region. In addition, an adaptive penalty function method is employed, which adaptively adjust the penalty coefficient so as to guarantee a moderate penalty. By solving 11 benchmark test functions and two engineering problems, experiment results indicate that the proposed method is reliable and efficient for solving constrained optimization problems. Also, it has great potential in handling many engineering problems with constraints, even with equations.