高校排课系统优化模型的可行性研究

以高校排课问题为研究背景,建立排课系统的数学模型,运用遗传算法,产生满足硬条件的初始解,并以软条件为适应度,进行全局最优解的搜索.运用Matlab软件编写程序,并以新疆大学数学与系统科学学院排课为实例,结合多年排课经验,验证了优化模型结果的可行性、并给模型提出了改进方案,为进一步方便教师、学生和教学管理人员.为完善教务排课系统提供了理论依据.     

搜索区域自动变更的智能水滴算法实训课排课优化

排课问题是NP完全问题,高校实训室排课需考虑实训设备配置及教学改革"走班制"专业选修课所增加的排课复杂度.将高校实训室排课问题建模为硬约束目标及软约束优化满足问题,提出了经过改进的智能水滴算法,改进算法在路径寻优过程中根据待排课程的属性与当前排课状态,结合优化目标,自动进行跳转或围绕核心点变更搜索区域,有效解决了标准智能水滴算法搜索范围固定不利于算法搜索效率提升的问题.提出了预排序策略,减轻算法后期运行的阻力,在排课资源紧张的情况下,更好地实现收敛.通过改进智能水滴算法、标准智能水滴算法、遗传算法进行排课实验对比,验证了改进智能水滴算法在排课系统中的优化效果和高效性。     

自适应多种群回溯群居蜘蛛算法求解TSP问题

在群居蜘蛛优化算法中引入自适应决策半径,将蜘蛛种群动态地分成多个种群,种群内适应度不同的个体采取不同的更新方式.在筛选全局极值的基础上,根据进化程度执行回溯迭代更新,提出一种自适应多种群回溯群居蜘蛛优化算法,旨在提高种群样本多样性和算法全局寻优能力.函数寻优结果表明改进算法具有较快的收敛速度和较高的收敛精度.最后将其应用于TSP问题的求解.     

函数优化的小生境蝙蝠算法

针对蝙蝠算法易陷入局部最优解的缺点,利用小生境技术对蝙蝠算法进行了改进,提出一种小生境蝙蝠优化算法.算法基于小生境技术的适应度共享来分隔种群,引入了小生境排挤机制来保持种群多样性,在延续蝙蝠算法原有并行搜索等优势的基础上,提高了算法的金局搜索能力和局部收敛速度,具有可在不同邻域内发现多个解的特点.通过对一系列经典函数测试,并与已有算法进行比较,结果表明该算法在函数优化问题的求解中具有较高的计算效率和精度,以及较好的全局寻优能力.     

基于离散型荧火虫算法的高校智能排课的研究

针对传统排课方法排课效率低、成功率低、冲突率高等无法满足现代高校教务管理要求的现状,提出一种基于离散型荧火虫算法的智能排课模型.首先,根据教师、班级、课程、教室及授课时间要求建立一个多目标、多约束的排课数学模型,采用二分图完美匹配操作初始可行排课方案;然后,利用离散型荧火虫优化算法在可行方案中寻找最优排课方案;最后,通过Matlab仿真实验验证其可行性与有效性.     

一种基于极坐标编码的果蝇优化算法

针对果蝇优化算法易陷入早熟收敛、收敛速度慢、寻优精度低的缺点,提出一种基于极坐标编码的果蝇优化算法.为提高果蝇优化算法的寻优精度,采用极坐标编码的形式,以增加单个母体寻优空间表示方法的多样性,并使种群中的个体,在围绕个体的整个超球体内随机搜索,使个体的搜索范围更加广泛.在迭代寻优过程中,根据适应度值和概率调整极角,逐渐降低观测结果的不确定性.通过9个基准测试函数,对基于极坐标编码的果蝇优化算法进行仿真实验,结果表明了算法在收敛性和稳定性方面,优于其它5个优化算法,测试结果验证了极坐标编码方法的有效性和可行性.     

求解高维全局优化问题的改进飞蛾火焰优化算法

针对标准飞蛾火焰优化算法在求解高维全局优化问题时存在收敛速度慢、解精度低和易陷入局部最优等缺点,提出一种改进的飞蛾火焰优化算法(简记为IMFO).该算法首先引入动态惯性权重对飞蛾位置更新方程进行修改以平衡算法的勘探和开采能力.受差分进化算法启发,设计出一种新的随机差分变异策略,以帮助种群跳出局部最优.选取18个高维(100、500和1000维)全局优化问题进行数值测试,结果表明,在相同的适应度函数评价次数下,IMFO在收敛速度和求解精度指标上明显优于基本MFO算法和其他对比算法.     

一种改进适应度函数的遗传算法

针对遗传算法搜索导优中适应度函数的设计不当,将难以体现个体差异和选择操作的作用,从而造成早熟收敛的问题,构建了两种基于顺序的适应度函数的模型.适应度函数的设计使得在进化过程中控制选择压力,种群竞争力得到增强,早熟现象得到改善.并将改进的算法应用在复杂函数优化问题上,MATLAB优化结果表明,算法在种群多样性、搜索速度、计算精度上均有改善,推动遗传算法在工程领域的应用.     

一种改进的遗传k-means聚类算法

在经典的k-means聚类算法中,聚类数k必须事先给定,然而在现实中k很难被精确的确定.本文提出了一种改进的遗传k-means聚类算法,并构造了一个用来评价分类程度好坏的适应度函数,该适应度函数考虑的是在提高紧凑度(类内距)和分离度(类间距)的同时使得分类个数尽可能少.最后采用两个人工数据集和三个UCI数据集对k-means聚类算法(KM),遗传聚类算法(GA),遗传k-means聚类算法(GKM)和改进的遗传k-means聚类算法(IGKM)进行比较研究,比较的指标有类间距、类内距和分类正确率.研究证明改进的遗传k-means算法能够自动获取最佳聚类数k并且保持较高的正确率.     

基于混沌算法的改进布谷鸟搜索算法及其应用

对布谷鸟搜索算法的Lévy飞行机制进行简化,利用混沌算法改进种群初始化,并按适应度值进行排序,参考蛙跳算法对排在末位的若干个鸟巢进行随机变异,提出一种基于混沌算法的改进布谷鸟搜索算法.通过测试函数验证,并与其他算法进行比较,该算法显示出更好的搜索性能和精度,将其应用于水电站经济调度问题,与传统算法相比,效果更好.     

Backtracking and exchange of information: Methods to enhance a beam search algorithm for assembly line scheduling

Beam search (BS) is used as a heuristic to solve various combinatorial optimization problems, ranging from scheduling to assembly line balancing. In this paper, we develop a backtracking and an exchange-of-information (EOI) procedure to enhance the traditional beam search method. The backtracking enables us to return to previous solution states in the search process with the expectation of obtaining better solutions. The EOI is used to transfer information accumulated in a beam to other beams to yield improved solutions.     

Random backtracking in backtrack search algorithms for satisfiability

This paper proposes the utilization of randomized backtracking within complete backtrack search algorithms for propositional satisfiability (SAT). In recent years, randomization has become pervasive in SAT algorithms. Incomplete algorithms for SAT, for example the ones based on local search, often resort to randomization. Complete algorithms also resort to randomization. These include state-of-the-art backtrack search SAT algorithms that often randomize variable selection heuristics. Moreover, it is plain that the introduction of randomization in other components of backtrack search SAT algorithms can potentially yield new competitive search strategies. As a result, we propose a stochastic backtrack search algorithm for SAT, that randomizes both the variable selection and the backtrack steps of the algorithm. In addition, we relate randomized backtracking with a more general form of backtracking, referred to as unrestricted backtracking. Finally, experimental results for different organizations of randomized backtracking are described and compared, providing empirical evidence that the new search algorithm for SAT is a very competitive approach for solving hard real-world instances.     

基于关键链的敏捷软件开发多项目网络模型

敏捷软件开发因其效率和文档量远低于传统方法在一提出就得到广泛应用,但仍无法有效解决软件开发多项目管理中的资源受限调度问题.将关键链思想应用到包含多个项目的敏捷软件开发问题中,在分析敏捷软件开发多项目网络模型的基础上,建立了数学优化模型;提出了一种适宜敏捷软解开发的多项目网络迭代调度假设与规则,并设计了相应的算法,具体包括关键链选择算法和调度算法;最后进行了实例分析,所得结果与遗传算法的相比从52个单位时间的迭代周期减少到42,使得工期节省了近20%.     

Approximation of the distribution of convergence times for stochastic global optimisation

How long should we run a stochastic global optimisation algorithm such as simulated annealing? How should we tune such an algorithm? This paper proposes an approach to the study of these questions through successive approximation of a generic stochastic global optimisation algorithm with a sequence of stochastic processes, culminating in a backtracking adaptive search process. Our emerging understanding of backtracking adaptive search can thus be used to study the original algorithm. The first approximation, the averaged range process, has the same expected number of iterations to convergence as the original process.     

动态空间调度的混合遗传算法

提出了一种基于混合遗传算法的动态空间调度方法。首先利用遗传算法产生多个可行的分段调度序列,再采用动态决定分段位置的启发式算法——平均最大空闲矩形策略对遗传算法产生的调度序列进行解码。同时以完工时间和平台利用率的加权和作为适应度函数,充分考虑了空间调度问题所特有的动态性和时空关联性。遗传进化过程收敛后得到近似最优解,实现了调度方案的全局优化。对船厂实际生产数据进行了实证分析以及与其它算法的对比分析,证明了所提方法在空间调度问题上的有效性和实用性。     

A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic

In this article, a new memetic algorithm has been proposed to solve job shop scheduling problems (JSSPs). The proposed method is a genetic-algorithm-based approach combined with a local search heuristic. The proposed local search heuristic is based on critical operations. It removes the critical operations and reassigns them to a new position to improve the fitness value of the schedule. Moreover, in this article, a new fitness function is introduced for JSSPs. The new fitness function called priority-based fitness function is defined in three priority levels to improve the selection procedure. To show the generality of our proposed method, we apply it to three different types of job scheduling problems including classical, flexible and multi-objective flexible JSSPs. The experiment results show the efficiency of the proposed fitness function. In addition, the results show that incorporating local search not only offers better solutions but also improves the convergence rate. Compared to the state-of-the-art algorithms, the proposed method outperforms the existing methods in classical JSSPs and offers competitive solutions in other types of scheduling problems.     

Depth-first heuristic search for the job shop scheduling problem

We evaluate two variants of depth-first search algorithms and consider the classic job shop scheduling problem as a test bed. The first one is the well-known branch-and-bound algorithm proposed by P. Brucker et al. which uses a single chronological backtracking strategy. The second is a variant that uses partially informed depth-first search strategy instead. Both algorithms use the same heuristic estimation; in the first case, it is only used for pruning states that cannot improve the incumbent solution, whereas in the second it is also used to sort the successors of an expanded state. We also propose and analyze a new heuristic estimation which is more informed and more time consuming than that used by Brucker’s algorithm. We conducted an experimental study over well-known instances showing that the proposed partially informed depth-first search algorithm outperforms the original Brucker’s algorithm.     

非线性等式约束优化问题的仿射信赖域方法

提出非线性等式和有界约束优化问题的结合非单调技术的仿射信赖域方法. 结合信赖域方法和内点回代线搜索技术, 每一步迭代转到由一般信赖域子问题产生的回代步中且满足严格内点可行条件. 在合理的假设条件下, 证明了算法的整体收敛性和局部超线性收敛速率. 最后, 数值结果表明了所提供的算法具有有效性.     

A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar

A phased array radar (PAR) is used to detect new targets and update the information of those detected targets. Generally, a large number of tasks need to be performed by a single PAR in a finite time horizon. In order to utilize the limited time and the energy resources, it is necessary to provide an efficient task scheduling algorithm. However, the existing radar task scheduling algorithms can't be utilized to release the full potential of the PAR, because of those disadvantages such as full PAR task structure ignored, only good performance in one aspect considered and just heuristic or the meta-heuristic method utilized. Aiming at above issues, an optimization model for the PAR task scheduling and a hybrid adaptively genetic (HAGA) algorithm are proposed. The model considers the full PAR task structure and integrates multiple principles of task scheduling, so that multi-aspect performance can be guaranteed. The HAGA incorporates the improved GA to explore better solutions while using the heuristic task interleaving algorithm to utilize wait intervals to interleave subtasks and calculate fitness values of individuals in efficient manners. Furthermore, the efficiency and the effectiveness of the HAGA are both improved by adopting chaotic sequences for the population initialization, the elite reservation and the mixed ranking selection, as well as designing the adaptive crossover and the adaptive mutation operators. The simulation results demonstrate that the HAGA possesses merits of global exploration, faster convergence, and robustness compared with three state-of-art algorithms—adaptive GA, hybrid GA and highest priority and earliest deadline first heuristic (HPEDF) algorithm.     

DBR理论求解柔性作业车间调度问题

针对柔性作业车间调度完工时间最小问题,提出一种结合DBR(鼓-缓冲器-绳子)理论和改进遗传算法的方法。在问题初始化时,建立瓶颈机器识别机制改善初始化方法,提高初始解的质量;在运算过程中依据关键路径建立瓶颈机器的识别机制和调度策略。为了更好保留每代中的优良解,采用外部精英库对优良解进行解保留。运用提出的算法求解基准测试问题,实验结果验证了算法的可行性和有效性。