基于蚁群算法的粮食应急调度问题研究

为解决粮食应急调度问题,建立一个适合多应急点、多出救点的多目标优化模型。将“应急开始时间最早”和“出救点数目最少”作为优化目标,考虑每个应急点的紧急重要程度和粮食自身的消耗特点,引入相应因子。应用蚁群算法对模型进行求解,给出算法求解的具体步骤。数值算例表明,该模型可提高粮食的应急调度效率。     

蚁群优化与模糊聚类结合的文本聚类研究

针对模糊文本聚类算法（FCM）对输入顺序以及初始点敏感的问题,提出了一种使用蚁群优化的模糊聚类算法（FACA）。该算法采用蚁群聚类算法（ACA）找到聚类的初始中心点,以解决模糊聚类的输入顺序以及初始点敏感等问题。模糊文本聚类算法的线性复杂度使其更便于在计算机实现。与经典的基本模糊聚类以及蚁群聚类在真实数据集上仿真相比较,结果表明经蚁群优化过的模糊聚类算法（FACA）效果更有效,更适合应用于大型的数据集。     

基于模糊聚类的RDC选址的研究

随着物流产业的发展,RDC选址问题已经成为物流产业中一个重要的研究问题。有一些解决方法,但是都有不足的地方。为了更好地解决这个问题,文中用模糊聚类的方法来研究RDC选址的问题。先用模糊聚类把要分类的点按照一定的标准进行分类,然后在每个类中用线性规划进行具体的选址,这样可以大大降低计算量,而且在聚类的时候可以得到不同的方案的聚类大小,可以给决策者一个选择的空间。     

基于混合算法的多停靠点物流路径规划仿真

现有的多停靠点物流路径规划方法未考虑配送中心的位置,无法合理调整停靠点,导致规划不合理,配送效率低的问题.提出一种基于混合算法的多停靠点物流路径规划方法.先对多停靠点物流路劲规划问题展开描述,计算停靠点间的配送用时和停靠时间,构建路径规划数学模型.采用改进蚁群算法求解所选路径的选取概率,物流配送中心选取,结合改进可见度找到配送中心,依据2opt算法原理调整停靠点,利用遗传聚类算法对停靠点类别进行聚类,得到聚类编码的适应度,结合路径规划数学模型,判定出最佳路径.仿真结果表明,所提方法的配送效率最高,规划效果最佳.     

基于模糊聚类的RDC选址的研究

随着物流产业的发展,RDC选址问题已经成为物流产业中一个重要的研究问题.有一些解决方法,但是都有不足的地方.为了更好地解决这个问题,文中用模糊聚类的方法来研究RDC选址的问题.先用模糊聚类把要分类的点按照一定的标准进行分类,然后在每个类中用线性规划进行具体的选址,这样可以大大降低计算量,而且在聚类的时候可以得到不同的方案的聚类大小,可以给决策者一个选择的空间.     

基于无向超图的多蚁群聚类组合算法

介绍了一种基于无向超图的多蚁群聚类组合算法,该算法将单蚁群聚类算法的结果聚类组合成多蚁群聚类算法,用无向超图表示,结合超图划分算法Hmetis得到最终的聚类结果。文中给出了实验数据集和实验结果,证明该算法可以提高聚类效果并且减少孤立点。     

基于变步长蚁群算法的移动机器人路径规划

针对传统蚁群算法以及双层蚁群算法在路径规划中存在搜索效率低、收敛性较慢以及成本较高的问题,本文提出了变步长蚁群算法。该算法扩大蚁群可移动位置的集合,通过对跳点的选择以达到变步长策略,有效缩短移动机器人路径长度;初始化信息素采用不均匀分布,加强起点至终点直线所涉及到栅格的信息素浓度平行地向外衰减;改进启发式信息矩阵,调整移动机器人当前位置到终点位置的启发函数计算方法。试验结果表明:变步长蚁群算法在路径长度及收敛速度两方面均优于双层蚁群算法及传统蚁群算法,验证了变步长蚁群算法的有效性和优越性,是解决移动机器人路径规划问题的有效算法。     

蚁群算法物流配送中心选址优化仿真研究

研究物流配送选址优化调度问题。为了有效节约车辆运输成本,应选择最优路径。城市车辆调度路径选择,存在路网复杂性,参数设置较多,传统的调度算法存在计算复杂度高,不利于实际应用。为解决优化选址问题,提出了一种改进的蚁群优化物流配送选址方法。算法把求得的解首先分解为解对,然后通过改进的蚁群优化算法将解对从不确定性转变成确定性问题,可以大大的降低求解过程。通过仿真表明,提出的优化算法不但降低了计算的复杂度,优化了选址模型,而且为解决物流选址问题提供了新的有效途径。     

蚁群聚类组合方法的研究

基于蚁群算法的聚类算法已经在当前的数据挖掘研究中得到应用。针对蚁群聚类算法早期出现的缺点,提出一种蚁群聚类组合方法使其得以改进。改进思路是引入K-means作为蚁群算法的预处理过程。通过K-means快速、粗略地确定聚类中心,利用K-means方法的结果作为初值,再进行蚁群算法聚类。有效地解决了蚁群算法早期收敛过慢等问题。     

基于时空聚类求解带容积约束的选址-路径问题

选址-路径问题是供应链管理和物流系统规划中的一个重要问题,对总成本具有十分重要的影响.对考虑配送中心容积约束的带时间窗的选址-路径问题进行研究,建立以总成本最小和客户满意度最大为目标的多目标规划模型,提出两阶段算法对其进行求解.首先,利用k-means聚类算法确定配送中心选址;然后,提出一种基于时间-空间双因素的客户划分方法以确定配送中心所服务客户;最后,利用粒子群算法对各配送中心的配送路径进行规划.数值算例表明,所提出的算法较其他已有算法,均能有效地降低物流运作总成本及总配送路径长度,为解决带容积约束及时间窗的选址-路径问题提供了一种新的解决思路.     

High speed ant colony optimization CMOS chip

Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies’ behavior. This paper presents design and CMOS implementation of the ant colony optimization based algorithm for solving the TSP problem. In order to implement ant colony optimization algorithm in CMOS, we will present a new algorithm. This algorithm is based on the original ant colony optimization but it can be implemented in CMOS. Briefly, pheromone matrix is transformed on the chip area and ants move up-down through the pheromone matrix and they make their decisions. Finally ants select a global path. In previous researches only pheromone values is used, but select the next city in this paper is based on heuristics value and pheromone value. In definition of problem, we use heuristics value as a matrix. Previous researches could not be used for wide type of optimization problem but our chip gives heuristics value initially and we can change initial value of heuristics value according to the optimization problem so this capability increases the flexibility of ACO chip. Simple circuit is used in blocks of our chip to increase the speed of convergence of ACO chip. We use Linear Feedback Shift Register (LSFR) circuit for random number generator in ACO chip. ACO chip has capability of solving the big TSP problem. ACO chip is simulated by HSPICE software and simulation results show the good performance of final chip.     

Ant colony optimization

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Ant colony optimization exploits a similar mechanism for solving optimization problems. From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. The goal of this article is to introduce ant colony optimization and to survey its most notable applications     

Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

An auto controlled ant colony optimization algorithm controls the behavior of the ant colony algorithm automatically based on a priori heuristic. During the experimental study of auto controlled ACO algorithm on grid scheduling problem, it was observed that the induction of lazy ants not only reduces the time complexity of the algorithm but also produces better results on the given objectives. Lazy ants are basically a mutated version of active ants that remain alive till the fitter lazy ants are generated in the successive generations. This work presents an improved auto controlled ACO algorithm using the lazy ant concept. Performance study reveals the efficacy and the efficiency achieved by the proposed algorithm. A comparative study of the proposed method with some other recent meta-heuristics such as auto controlled ant colony optimization algorithm, genetic algorithm, quantum genetic algorithm, simulated annealing and particle swarm optimization for grid scheduling problem exhibits so.     

Hardware-oriented ant colony optimization

A new kind of ant colony optimization (ACO) algorithm is proposed that is suitable for an implementation in hardware. The new algorithm – called Counter-based ACO – allows to systolically pipe artificial ants through a grid of processing cells. Various features of this algorithm have been designed so that it can be mapped easily to field-programmable gate arrays (FPGAs). Examples are a new encoding of pheromone information and a new method to define the decision sequence of ants. Experimental results that are based on simulations for the traveling salesperson problem and the quadratic assignment problem are presented to evaluate the proposed techniques.     

蚁群算法一阶欺骗性问题的时间复杂度分析

文中研究蚁群算法求解欺骗性问题时的时间复杂度。以蚁群算法一阶欺骗性问题n-bit陷阱问题为例, 证明使用信息素带限的最大最小蚁群算法求解n-bit陷阱问题达到最优解的时间复杂度为O(n²mlnn),其中n为问题的规模,m为蚂蚁的个数。实验结果验证上述结论的正确性。     

Ant colony optimization for routing and load-balancing: survey and new directions

Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigating stagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include: evaporation, aging, pheromone smoothing and limiting, privileged pheromone laying and pheromone-heuristic control. The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNet and its extensions, as well as ASGA and SynthECA. Discussions on new directions include an ongoing work of the authors in applying multiple ant colony optimization in load-balancing.     

基于混合蚁群算法的WTA问题求解

武器-目标分配问题(Weapon-TargetAssignmentProblem)是一种典型的NP问题。该文提出了一种基于遗传算法和蚁群算法的混合算法(GAACO)以解决武器-目标分配问题。首先,使用遗传算法对火力分配问题形成初始解;然后,将遗传算法的结果传递给改进的蚁群算法,对问题求精确解。实验结果表明该算法求精度优于遗传算法,时间性能优于传统蚁群算法。     

An ACO-RFD hybrid method to solve NP-complete problems

In this paper we hybridize ant colony optimization (ACO) and river formation dynamics (RFD), two related swarm intelligence methods. In ACO, ants form paths (problem solutions) by following each other’s pheromone trails and reinforcing trails at best paths until eventually a single path is followed. On the other hand, RFD is based on copying how drops form rivers by eroding the ground and depositing sediments. In a rough sense, RFD can be seen as a gradient-oriented version of ACO. Several previous experiments have shown that the gradient orientation of RFD makes this method solve problems in a different way as ACO. In particular, RFD typically performs deeper searches, which in turn makes it find worse solutions than ACO in the first execution steps in general, though RFD solutions surpass ACO solutions after some more time passes. In this paper we try to get the best features of both worlds by hybridizing RFD and ACO. We use a kind of ant-drop hybrid and consider both pheromone trails and altitudes in the environment. We apply the hybrid method, as well as ACO and RFD, to solve two NP-hard problems where ACO and RFD fit in a different manner: the traveling salesman problem (TSP) and the problem of the minimum distances tree in a variable-cost graph (MDV). We compare the results of each method and we analyze the advantages of using the hybrid approach in each case.     

云计算环境下基于蚁群优化算法的资源调度策略

针对当前云计算环境中节点规模巨大,单个节点资源配置较低,寻找有效计算资源效率不高的缺点,文中在Google公司的Map/Reduce框架上提出了两个基于蚁群优化的资源调度策略ACO1和ACO2,并在这两个资源调度策略中引入双向蚂蚁机制。在该双向蚂蚁机制中蚂蚁通过相互交流,能够快速地发现合适的虚拟机资源,从而使得Master节点能够快速地为用户任务分配虚拟机。实验结果表明这两个利用了双向蚂蚁机制的资源调度策略显著减少了为用户任务寻找虚拟机的时间,从而使得用户任务能够更快地获得虚拟机,保证用户作业能够按时完成。     

Diversity control in ant colony optimization

Optimization inspired by cooperative food retrieval in ants has been unexpectedly successful and has been known as ant colony optimization (ACO) in recent years. One of the most important factors to improve the performance of the ACO algorithms is the complex trade-off between intensification and diversification. This article investigates the effects of controlling the diversity by adopting a simple mechanism for random selection in ACO. The results of computer experiments have shown that it can generate better solutions stably for the traveling salesmen problem than AS_rank which is known as one of the newest and best ACO algorithms by utilizing two types of diversity.