Efficient parallel algorithms for computing all pair shortest paths in directed graphs

We present parallel algorithms for computing all pair shortest paths in directed graphs. Our algorithm has time complexityO(f(n)/p+I(n)logn) on the PRAM usingp processors, whereI(n) is logn on the EREW PRAM, log logn on the CCRW PRAM,f(n) iso(n ³). On the randomized CRCW PRAM we are able to achieve time complexityO(n ³/p+logn) usingp processors. A preliminary version of this paper was presented at the 4th Annual ACM Symposium on Parallel Algorithms and Architectures, June 1992. Support by NSF Grant CCR 90-20690 and PSC CUNY Awards #661340 and #662478.     

Solving shortest paths efficiently on nearly acyclic directed graphs

Shortest path problems can be solved very efficiently when a directed graph is nearly acyclic. Earlier results defined a graph decomposition, now called the 1-dominator set, which consists of a unique collection of acyclic structures with each single acyclic structure dominated by a single associated trigger vertex. In this framework, a specialised shortest path algorithm only spends delete-min operations on trigger vertices, thereby making the computation of shortest paths through non-trigger vertices easier. A previously presented algorithm computed the 1-dominator set in O(mn) worst-case time, which allowed it to be integrated as part of an O(mn+nrlogr) time all-pairs algorithm. Here m and n respectively denote the number of edges and vertices in the graph, while r denotes the number of trigger vertices. A new algorithm presented in this paper computes the 1-dominator set in just O(m) time. This can be integrated as part of the O(m+rlogr) time spent solving single-source, improving on the value of r obtained by the earlier tree-decomposition single-source algorithm. In addition, a new bidirectional form of 1-dominator set is presented, which further improves the value of r by defining acyclic structures in both directions over edges in the graph. The bidirectional 1-dominator set can similarly be computed in O(m) time and included as part of the O(m+rlogr) time spent computing single-source. This paper also presents a new all-pairs algorithm under the more general framework where r is defined as the size of any predetermined feedback vertex set of the graph, improving the previous all-pairs time complexity from O(mn+nr²) to O(mn+r³).     

Improved algorithm for all pairs shortest paths

We present an improved algorithm for all pairs shortest paths. For a graph of n vertices our algorithm runs in O(n³(loglogn/logn)^5/7) time. This improves the best previous result which runs in O(n³(loglogn/logn)^1/2) time.     

Improved algorithm for finding next-to-shortest paths

We study the problem of finding the next-to-shortest paths in a weighted undirected graph. A next-to-shortest (u,v)-path is a shortest (u,v)-path amongst (u,v)-paths with length strictly greater than the length of the shortest (u,v)-path. The first polynomial algorithm for this problem was presented in [I. Krasikov, S.D. Noble, Finding next-to-shortest paths in a graph, Inform. Process. Lett. 92 (2004) 117-119]. We improve the upper bound from O(n³m) to O(n³).     

An efficient parallel algorithm for shortest paths in planar layered digraphs

Computing shortest paths in a directed graph has received considerable attention in the sequential RAM model of computation. However, developing a polylog-time parallel algorithm that is close to the sequential optimal in terms of the total work done remains an elusive goal. We present a first step in this direction by giving efficient parallel algorithms for shortest paths in planar layered digraphs.We show that these graphs admit special kinds of separators calledone- way separators which allow the paths in the graph to cross it only once. We use these separators to give divide- and -conquer solutions to the problem of finding the shortest paths between any two vertices. We first give a simple algorithm that works in the CREW model and computes the shortest path between any two vertices in ann-node planar layered digraph in timeO(log² n) usingn/logn processors. We then use results of Aggarwal and Park [1] and Atallah [4] to improve the time bound toO(log² n) in the CREW model andO(logn log logn) in the CREW model. The processor bounds still remain asn/logn for the CREW model andn/log logn for the CRCW model.Support for the first and third authors was provided in part by a National Science Foundation Presidential Young Investigator Award CCR-9047466 with matching funds from IBM, by NSF Research Grant CCR-9007851, by Army Research Office Grant DAAL03-91-G-0035, and by the Office of Naval Research and the Advanced Research Projects Agency under Contract N00014-91-J-4052, ARPA, Order 8225. Support for the second author was provided in part by NSF Research Grant CCR-9007851, by Army Research Office Grant DAAL03-91-G-0035, and by the Office of Naval Research and the Advanced Research Projects Agency under Contract N00014-91-J-4052 and ARPA Order 8225.     

一种求解函数优化的混合蚁群算法*

将遗传算法与蚁群算法中的协同模型进行有机结合,在蚁群算法中引入交叉、变异、选择算子来改进基本蚁群算法,克服了蚁群算法不太适合求解连续空间优化问题的缺陷。通过测试函数表明该方法具有较好的收敛速度和稳定性,求解结果好于遗传算法。     

多目标卫星数传调度蚁群优化算法

卫星数传调度问题具有任务多、资源少、调度约束复杂等特点,为满足多目标优化调度的理论和现实需要,提出了多目标卫星数传调度蚁群优化算法。算法建立了基于任务调度关系的解构造图,提出了用于可行解构造的自适应伪随机概率决策模型,以及基于Pareto解偏离度的全局信息素更新策略。仿真结果表明,算法具有较好的Pareto前沿收敛性,各优化目标都能得到较好的指标评价值,所获得的Pareto解集规模适度,Pareto解的多样性、分布均匀性和散布范围都较好。     

An optimal algorithm for shortest paths on weighted interval and circular-arc graphs,with applications

We give the first linear-time algorithm for computing single-source shortest paths in a weighted interval or circular-arc graph, when we are given the model of that graph, i.e., the actual weighted intervals or circular-arcsand the sorted list of the interval endpoints. Our algorithm solves this problem optimally inO(n) time, wheren is the number of intervals or circular-arcs in a graph. An immediate consequence of our result is anO(qn + n logn)-time algorithm for the minimum-weight circle-cover problem, whereq is the minimum number of arcs crossing any point on the circle; then logn term in this time complexity is from a preprocessing sorting step when the sorted list of endpoints is not given as part of the input. The previously best time bounds were0(n logn) for this shortest paths problem, andO(qn logn) for the minimum-weight circle-cover problem. Thus we improve the bounds of both problems. More importantly, the techniques we give hold the promise of achieving similar (logn)-factor improvements in other problems on such graphs.The research of M. J. Atallah was supported in part by the Leonardo Fibonacci Institute, Trento, Italy, by the Air Force Office of Scientific Research under Contract AFOSR-90-0107, and by the National Science Foundation under Grant CCR-9202807. D. Z. Chen's research was supported in part by the Leonardo Fibonacci Institute, Trento, Italy. The research of D. T. Lee was supported in part by the Leonardo Fibonacci Institute, Trento, Italy, by the National Science Foundation, and the Office of Naval Research under Grants CCR-8901815, CCR-9309743, and N00014-93-1-0272.     

用改进蚁群算法求解函数优化问题

提出将蚁群算法用于求解函数优化问题的新方法。使用一定数量的蚂蚁在解空间中首先随机搜索,然后模拟蚂蚁觅食的方式,更新搜索路径上的信息素,按照转移概率来决定搜索方向,即通过信息素来指引搜索,最后搜索收敛于各个全局最优解。给出了基于此思想的具体算法,并通过计算示例仿真说明了该算法的有效性,表明该算法可以同时快速收敛发现多个全局最优解,并保持稳定。     

一种基于改进蚁群优化的QoS路由算法

蚁群优化（Ant Colony Optimization,AC0）是一种新型的分布式仿生优化算法,可有效地用来解决组合优化问题,而网络路由优化问题则正是组合优化问题当中的一种。因此,本文首先分析了常用路由算法与蚁群优化的基本原理,根据网络路由优化问题与蚁群优化算法的许多匹配特性,提出了一种基于改进蚁群优化的QoS路由算法（Route Algorithm based on Improved Ant Colony Optimlzation,RAIAC0）。最后,通过实验分析,对其可行性进行了证明。     

Enhancing data parallelism for Ant Colony Optimization on GPUs

Graphics Processing Units (GPUs) have evolved into highly parallel and fully programmable architecture over the past five years, and the advent of CUDA has facilitated their application to many real-world applications. In this paper, we deal with a GPU implementation of Ant Colony Optimization (ACO), a population-based optimization method which comprises two major stages: tour construction and pheromone update. Because of its inherently parallel nature, ACO is well-suited to GPU implementation, but it also poses significant challenges due to irregular memory access patterns. Our contribution within this context is threefold: (1) a data parallelism scheme for tour construction tailored to GPUs, (2) novel GPU programming strategies for the pheromone update stage, and (3) a new mechanism called I-Roulette to replicate the classic roulette wheel while improving GPU parallelism. Our implementation leads to factor gains exceeding 20x for any of the two stages of the ACO algorithm as applied to the TSP when compared to its sequential counterpart version running on a similar single-threaded high-end CPU. Moreover, an extensive discussion focused on different implementation paths on GPUs shows the way to deal with parallel graph connected components. This, in turn, suggests a broader area of inquiry, where algorithm designers may learn to adapt similar optimization methods to GPU architecture.     

蚁群优化的无线传感器网络分簇方法研究

分簇算法是无线传感器网络中减少网络能量消耗的一种重要方法。为了有效使用无线传感器节点有限的能量,将蚁群优化算法应用于无线传感器网络的路径选择,利用蚁群的动态适应性和寻优能力,在分簇产生的簇头节点之间找到最优路径,进而达到均衡网络负载、延长整个网络寿命的目的。模拟仿真实验结果表明了该算法的可行性和有效性。     

基于改进蚁群算法的方向过电流保护整定优化

本文提出了基于蚁群优化算法的方向过电流保护整定配合的优化模型.首先说明了方向过电流保护的时间特性,然后建立了方向过电流整定优化模型.优化目标是所有主保护动作时间之和最小,考虑了主后备保护配合约束、保护动作时间约束、启动电流约束等.本文所提方向过电流保护为非线性优化问题,提出利用改进蚁群优化算法来求解该模型.最后本文利用...     

A survey: Ant Colony Optimization based recent research and implementation on several engineering domain

Ant Colony Optimization (ACO) is a Swarm Intelligence technique which inspired from the foraging behaviour of real ant colonies. The ants deposit pheromone on the ground in order to mark the route for identification of their routes from the nest to food that should be followed by other members of the colony. This ACO exploits an optimization mechanism for solving discrete optimization problems in various engineering domain. 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. This paper review varies recent research and implementation of ACO, and proposed a modified ACO model which is applied for network routing problem and compared with existing traditional routing algorithms.     

基于量子蚁群算法的数据库连接查询优化

连接查询优化是提高数据库性能的关键技术,针对数据库连接查询优化效率低的难题,提出一种量子蚁群算法的数据库连接查询优化方法（QACA）.首先,将数据库连接查询计划左深树看作一个蚂蚁,然后,利用量子旋转门更新各路径信息素,并利用混沌变异策略保持种群多样性,通过蚂蚁之间的信息交流找到数据库连接查询最优计划,最后,进行数据库连接查询优化实例分析.结果表明,QACA是解决数据库连接查询优化的有效途径,获得理想的数据库连接查询计划,具有实际意义.     

应用蚁群算法求解TSP

介绍了一种求解复杂TSP的蚁群算法,阐述了该算法的基本原理、模型以及实现过程,并介绍了蚁群算法在旅行商问题(TSP)中的应用思路。     

遗传-蚁群融合算法在离散型优化中的研究及实现

主要针对离散型数学模型的优化问题,分析使用遗传和蚁群算法的优缺点,并克服遗传算法、蚁群算法各自的局限性,发挥其优势,通过遗传-蚁群融合算法进行优化计算。在研究过程中,采用C#语言实现融合算法,并定义标准输入和输出结构。利用油田措施优化应用案例进行了对比实验验证,结果表明,融合算法能有效地发挥遗传、蚁群算法的优点,运算速度及求解效率均较理想。     

Ant Colony Optimization-based method for optic cup segmentation in retinal images

An accurate detection of the cup region in retinal images is necessary to obtain relevant measurements for glaucoma detection. In this work, we present an Ant Colony Optimization-based method for optic cup segmentation in retinal fundus images. The artificial agents will construct their solutions influenced by a heuristic that combines the intensity gradient of the optic disc area and the curvature of the vessels. On their own, the exploration capabilities of the agents are limited; however, by sharing the experience of the entire colony, they are capable of obtaining accurate cup segmentations, even in images with a weak or non-obvious pallor. This method has been tested with the RIM-ONE dataset, yielding an average overlapping error of 24.3% of the cup segmentation and an area under the curve (AUC) of 0.7957 using the cup to disc ratio for glaucoma assessment.     

基于蚁群优化算法的旋转货架拣选路径规划

给出自动化立体仓库单拣选台分层水平旋转货架系统的数学模型,提出一种改进的蚁群优化算法,用于解决货物拣选路径规划问题。该算法能快速找到最优货物拣选路径,得到的解质量较高且计算时间短。仿真结果表明,该方法适用于求解中小规模货物拣选路径的规划问题,可以提高自动存储作业效率。