用改进的EAX算法求解TSP问题的研究

TSP问题是一个经典的NP问题,它要求解一条经过连通网络的所有顶点当且仅当一次且距离最短的回路,即距离最短的Hamilton回路问题。本文在研究利用遗传算法求解TSP问题的基础上,重点论述EAX算法并对E-set选择策略加以改进,以期改进算法的迭代时间。     

一种抗遮挡的多运动目标跟踪改进算法

由于传统空间欧氏距离最短法难以解决遮挡问题,提出一种固定场景下抗遮挡的对多个运动目标进行实时检测和跟踪的算法.在分析传统帧差算法的优缺点的基础上对其进行了改进,引入空间滤波和区域填充.介绍了传统空间欧氏距离最短法,分析了它的缺点.用带状态参量的空间欧氏距离最短法对每个视频运动目标质心进行关联,监测每个视频运动对象的运动状态、运动轨迹.通过实验证明,该方法在改进传统欧式距离最短法的基础上,能实时有效得跟踪运动目标.     

求解绝对值距离Steiner最小树的改进元胞蚂蚁算法

绝对值距离Steiner最小树问题是在集成电路布线等领域应用广泛的属于NP难的经典组合优化问题,由于该问题的搜索空间与元胞自动机的结构相似,设计了求解绝对值距离Steiner最小树问题的改进的元胞蚂蚁算法。经大量数据实验表明,该算法要比最小生成树平均改进15%,优于多数已有的基于最小生成树的近似算法,验证了算法的实用性。     

基于性价比改进的网格调度算法

在动态、异构的复杂网格环境中,任务调度算法已被证明是一个NP难问题.Min-Min调度算法是研究其它调度算法的基础之一.在分析Min-Min调度算法的基础上,指出该算法的缺陷:负载不均衡并且调度的过程中没有考虑费用的问题.针对这两个方面,提出了一种基于性价比改进的调度算法,通过分析表明,改进后的算法在费用、负载平衡度方面有了很大的提高,并且节省了很大一部分费用,说明改进后的算法在一定程度上提高了算法的效率,提高了网络的整体性能和总体吞吐量.     

TPR-树性能优化研究

为了降低TPR-树的时间复杂度、提高查询效率,提出结点分裂的改进算法及基于距离的结构调整策略.改进算法把TPBR(time-parametedzed bounding rectangle)在某时阃段内的周长定积分作为代价函数,并在投影定积分值最大的轴上进行结点分裂,做到了同时兼顾移动对象的空间属性与速度属性.在结构调整策略中通过删除结点中移动距离超过阈值的记录,从而使TPBRs的扩张速度变慢,在一定程度上抑制了TPBRs之间的重叠.实验结果表明,与原TPR-树结点分裂算法相比,改进后的结点分裂算法的运行时间降低了5～8倍,查询性能至少提高了50%,而且,在此基础上应用基于距离的结构调整策略使查询性能进一步提高约10%.     

求解货郎担问题的量子进化算法

货郎担问题属于NP完全问题,对它的近似求解方法主要是智能算法及线性规划,但其中的基本量子进化算法易陷于局部最优解。为此,提出一种新的量子进化算法,结合乡村货郎运输问题,对算法进行测试。结果表明,该算法在全局寻优能力及种群多样性方面均比传统算法有所改进,是求解乡村货郎担问题的一种有效算法。     

基于余弦距离选取初始簇中心的文本聚类研究

文本聚类是文本信息进行有效组织、摘要和导航的重要手段,其中基于余弦相似度的K-means算法是最重要且使用最广泛的文本聚类算法之一。针对基于余弦相似度的K-means算法改进方案设计困难,且众多优异的基于欧氏距离的K-means改进方法无法适用的问题,对余弦相似度与欧氏距离的关系进行探讨,得到标准向量前提下二者的转化公式,并在此基础上定义一种与欧氏距离意义相近关系紧密的余弦距离,使原有基于欧氏距离的K-means改进方法可通过余弦距离迁移到基于余弦相似度的K-means算法中。在此基础上理论推导出余弦K-means算法及其拓展算法的簇内中心点计算方法,并进一步改进了聚类初始簇中心的选取方案,形成新的文本聚类算法MCSKM++。通过实验验证,该算法在迭代次数减少、运行时间缩短的同时,聚类精度得到提高。     

基于跳跃辅助工作策略的混流装配线排产优化

为了使混流装配线高效运作, 研究了一类基于跳跃辅助工作策略的混流装配线排产优化问题. 以同时优化空闲费用和辅助工作费用为目标, 建立了一类基于跳跃辅助工作策略的混流装配线排产优化模型, 给出了执行跳跃辅助工作策略的一个必要条件和辅助工作费用的一个下界. 然后证明了该类优化问题是强NP难的, 由于该问题的强NP难性, 提出了一种嵌入式变邻域类电磁机制(Variable neighborhood search-electromagnetism-like mechanism, VNS-EM)混合算法求解该模型, 为了避免算法陷入局部最优, 在类电磁机制算法的每次迭代过程中嵌入改进的变邻域搜索算法, 利用变邻域搜索算法较好的局部搜索能力对最好个体的邻域进行精细搜索, 从而提高了解的质量. 仿真结果验证了该方法的可行性和有效性.     

基于围线分层扫描的完全欧氏距离变换算法

围线扫描欧氏距离变换算法是一种快速的完全欧氏距离变换算法,其时间复杂度达到最优,但需在围线区域进行全局搜索,计算时间并未优化。针对此问题,提出了一种基 于围线分层扫描的完全欧氏距离算法。该算法首先根据中心像素的围线性质对二值图像像素点进行重新分类,然后按照围线区域像素与中心像素的空间关系,对中心像素的围线区 域进行分层搜索,并给出了搜索的终止条件。该算法保持了最优的时间复杂度,可通过定量分析单个像素的计算时间来证明其计算时间已得到优化。实验结果表明,该算法能够得到 准确的欧氏距离图像,且运行速度快。     

应用改进的遗传算法求解TSP问题

旅行商问题,也称货郎担问题,属于完全NP问题,而遗传算法在解决组合排列问题方面占有很重要的地位.针对TSP问题,提出了一种改进的遗传算法.利用交换启发交叉算子和可变交叉概率实现局部搜索,加快算法的收敛速度,利用变换变异算子和可变变异概率维持群体的多样性防止算法早熟收敛.Java仿真实验结果表明,改进后的算法明显优于传统的遗传算法,说明该算法具有良好的有效性和可行性.     

A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES/spl I.bar/DECOMPOSE

In addition to the classical heuristic algorithms of operations research, there have also been several approaches based on artificial neural networks for solving the traveling salesman problem. Their efficiency, however, decreases as the problem size (number of cities) increases. A technique to reduce the complexity of a large-scale traveling salesman problem (TSP) instance is to decompose or partition it into smaller subproblems. We introduce an all-neural decomposition heuristic that is based on a recent self-organizing map called KNIES, which has been successfully implemented for solving both the Euclidean traveling salesman problem and the Euclidean Hamiltonian path problem. Our solution for the Euclidean TSP proceeds by solving the Euclidean HPP for the subproblems, and then patching these solutions together. No such all-neural solution has ever been reported.     

蚁群算法在TSP问题中的应用

TSP问题(旅行商问题)是组合优化问题中最经典的NP问题之一,蚁群算法是基于群体的一种仿生算法,为求解复杂的组合优化问题提供了一种新思路,本文讨论了如何用基本的蚁群算法来求解TSP问题。     

A GRASP heuristic using path‐relinking and restarts for the Steiner traveling salesman problem

The traveling salesman problem (TSP) is one of the most studied problems in combinatorial optimization. Given a set of nodes and the distances between them, it consists in finding the shortest route that visits each node exactly once and returns to the first. Nevertheless, more flexible and applicable formulations of this problem exist and can be considered. The Steiner TSP (STSP) is a variant of the TSP that assumes that only a given subset of nodes must be visited by the shortest route, eventually visiting some nodes and edges more than once. In this paper, we adapt some classical TSP constructive heuristics and neighborhood structures to the STSP variant. In particular, we propose a reduced 2‐opt neighborhood and we show that it leads to better results in smaller computation times. Computational results with an implementation of a GRASP heuristic using path‐relinking and restarts are reported. In addition, ten large test instances are generated. All instances and their best‐known solutions are made available for download and benchmarking purposes.     

PCB数控钻孔最佳走刀路线的建模与求解

当前的印刷电路板（ＰＣＢ）数控钻自动编程系统生成的钻孔路线并非最佳走刀路线。本文通过分析,将ＰＣＢ数控钻孔最佳走刀路线问题归结为大型ＴＳＰ问题,其目标函数定为钻头的总走刀时间最短。由于ＴＳＰ问题在理论上属于ＮＰ完备问题,很难用一般的算法求解。本文详细介绍了用模拟退火方法求解该问题的具体算法,并以此为基础开发了ＰＣＢ最优化的自动编程系统。     

A novel discrete bat algorithm for solving the travelling salesman problem

The travelling salesman problem (TSP) is one of the well-known NP-hard combinatorial optimization and extensively studied problems in discrete optimization. The bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. In this paper we extend a discrete bat-inspired algorithm to solve the famous TSP. Although many algorithms have been used to solve TSP, the main objective of this research is to investigate this discrete version to achieve significant improvements, not only compared to traditional algorithms but also to another metaheuristics. Moreover, this study is based on a benchmark dataset of symmetric TSP from TSPLIB library.     

求解TSP问题的离散捕鱼策略优化算法

针对典型离散优化问题旅行商问题,提出了一种离散捕鱼策略优化算法。结合TSP问题的特点,首先给出渔夫个体的离散编码方法,并在此基础上提出相异集和交换操作的基本概念;然后对渔夫个体之间的距离进行重新定义,并对渔夫个体的几种搜索策略进行重新描述;最后在TSPLIB标准库中选取3个算例对算法进行性能测试。数值仿真实验结果表明,对于求解TSP问题,离散捕鱼策略优化算法具有求解精度高、稳定性好、运行速度快等优点,为求解TSP问题提供了一种可行的新选择。     

Mission-oriented ant-team ACO for min–max MTSP

The multiple traveling salesman problem (mTSP) is a combinatorial optimization problem and an extension of the famous traveling salesman problem (TSP). Not only does the mTSP possess academic research value, but its application is extensive. For example, the vehicle routing problem and operations scheduling can all be reduced to mTSP solutions. The mTSP is an NP-hard problem, and multifaceted discussions of its solutions are worthwhile. This study assigned ants to teams with mission-oriented approaches to enhance ant colony optimization algorithms. Missions were appointed to ant teams before they departed (each ant had a different focal search direction). In addition to attempting to complete its own mission, each ant used the Max–Min strategy to work together to optimize the solution. The goal of appointing missions is to reduce the total distance, whereas the goal of using the max–min search method for paths was to achieve Min–Max , or the goal of labor balance. Four main elements were involved in the search process of the ant teams: mission pheromone, path pheromone, greedy factor, and Max–Min ant firing scheme. The experimental results revealed this novel approach to be constructive and effective.     

Localized Outlying and Boundary Data Detection in Sensor Networks

Given a set of sparsely distributed sensors in the Euclidean plane, a mobile robot is required to visit all sensors to download the data and finally return to its base. The effective range of each sensor is specified by a disk, and the robot must at least reach the boundary to start communication. The primary goal of optimization in this scenario is to minimize the traveling distance by the robot. This problem can be regarded as a special case of the traveling salesman problem with neighborhoods (TSPN), which is known to be NP-hard. In this paper, we present a novel TSPN algorithm for this class of TSPN, which can yield significantly improved results compared to the latest approximation algorithm.     

动态搜索算法求解时间依赖型旅行商问题研究

时间依赖型旅行商问题(TDTSP)是旅行商问题(TSP)的延伸.在该问题中,任意两节点间的旅行时间(成本)不仅取决于节点间的距离,还依赖于一天中具体时段或节点在哈密顿圈中所处的具体位置.对基于节点所处哈密顿圈中具体位置的TDTSP问题建立相应的数学模型,并提出求解该问题的动态搜索算法.通过实验仿真,验证了动态搜索算法优于目前在邻域搜索领域求解该问题最有效的动态规划启发式算法.     

PCB数控钻孔最佳走刀路线的建模与求解

目前,采用PCB数控钻自动编程系统生成的钻孔路线并非最佳走刀路线。通过分析,将PCB数控钻孔最佳走刀路线问题归结为大型TSP问题,其目标函数定为钻头的总走刀时间最短。由于TSP问题在理论上属于NP完备问题,因此很难用一般的算法求解。文中详细介绍了用模拟退火方法求解该问题的具体算法,并以上继基础开发了PCB的最优化的自动编程系统。