混合量子粒子群算法求解车辆路径问题

量子粒子群算法在求解车辆路径问题时一定程度上解决了基本粒子群算法收敛速度不够快的缺点,但是量子粒子群算法仍然存在容易陷入局部最优的缺点。利用混合量子粒子群算法对车辆路径问题进行求解,运用量子粒子群算法对初始粒子群的粒子进行更新,对粒子进行交叉操作,可以提高算法的全局搜索能力,进行变异操作,可以改善算法的局部搜索能力。以Matlab为工具进行仿真实验,实验结果表明改进后的算法在求解车辆路径问题时具有良好的性能,可以避免陷入局部最优,对比量子粒子群算法和遗传算法具有一定的优势。     

A routing-table-based adaptive and minimal routing scheme on network-on-chip architectures

In this paper, we present a routing algorithm that combines the shortest path routing and adaptive routing schemes for NoCs. In specific, routing follows the shortest path to ensure low latency and low energy consumption. This routing scheme requires routing information be stored in a series of routing tables created at the routers along the routing path from the source to the destination. To reduce the exploration space and timing cost for selecting the routing path, a routing list and routing table for each node are created off-line. Routing table is updated on-line to reflect the dynamic change of the network status to avoid network congestion. To alleviate the high hardware implementation cost associated with the routing tables, a method to help reduce the size of the routing tables is also introduced. Compared to the existing routing algorithms, the experimental results have confirmed that the proposed algorithm has better performance in terms of routing latency and power consumption.     

应用IPSO的无线传感器网络分簇路由算法

在基于分簇的无线传感器网络中,网络是通过附近传感器节点在转发信息到目的节点前进行冗余数据的融合实现节能,从而延长了网络的生命周期。但现存的算法在选择簇首节点的过程中由于忽略了邻居节点的状态信息,容易导致簇内节点过早出现盲节点的现象。进化类算法已经成功应用于许多方面,微粒群算法就是其中之一。提出了一种基于改进型微粒群算法的无线传感器网络分簇路由算法来优化分簇过程。簇首节点的选取综合考虑候选节点和邻居节点的状态信息。仿真结果表明算法的性能得到了较好的改善,并延长了网络的生命周期。     

A protocol synthesis method for fault-tolerant multipath routing

This paper proposes a new synthesis method for generating fault-tolerant multipath routing protocols. The protocol is defined as fault-tolerant if messages can be rerouted by using another path when a communication channel fails. The routing protocols obtained adopt a multipath routing function, augmented with routing table, where each table stores the next nodes for multipath routing, and updates the tables according to the network topology changes. Additionally, the routing protocol can attain flexibility by the multipath routing mechanism in the sense that only a small amount of change is needed for the change of network topology. We also briefly describe an extension of the proposed method for generating multicast routing protocols.     

Graceful deadlock-free fault-tolerant routing algorithm for 3D Network-on-Chip architectures

Three-Dimensional Networks-on-Chip (3D-NoC) has been presented as an auspicious solution merging the high parallelism of Network-on-Chip (NoC) interconnect paradigm with the high-performance and lower interconnect-power of 3-dimensional integration circuits. However, 3D-NoC systems are exposed to a variety of manufacturing and design factors making them vulnerable to different faults that cause corrupted message transfer or even catastrophic system failures. Therefore, a 3D-NoC system should be fault-tolerant to transient malfunctions or permanent physical damages.     

求解矩形包络问题的量子行为粒子群优化算法

介绍了一种利用量子行为粒子群算法（QPSO）求解矩形包络的方法。矩形包络是将二维不规则形状样片用它们的最佳包络矩形来代替,是服装排料的第一步。实验结果表明量子行为粒子群算法比粒子群算法,遗传算法能更好地解决求二维不规则形状样片的矩形包络的问题。     

带时间窗车辆路径问题的改进粒子群算法研究

设计了一种引入局部近邻机制并且能够优化不可行解的粒子群算法。该算法将粒子群分成相互重叠的子群,在各个子群内寻找近邻,提高了粒子的学习功能和寻找近邻的速度;同时将产生的不可行解进行局部优化,增强了粒子寻找最优的能力。实验结果表明：该算法可以快速求得带时间窗车辆路径问题的满意解。     

Constrained Portfolio Selection using Particle Swarm Optimization

This paper presents a novel heuristic method for solving an extended Markowitz mean–variance portfolio selection model. The extended model includes four sets of constraints: bounds on holdings, cardinality, minimum transaction lots and sector (or market/class) capitalization constraints. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio is equal to a predefined number. The sector capitalization constraints reflect the investors’ tendency to invest in sectors with higher market capitalization value to reduce their risk of investment.The extended model is classified as a quadratic mixed-integer programming model necessitating the use of efficient heuristics to find the solution. In this paper, we propose a heuristic based on Particle Swarm Optimization (PSO) method. The proposed approach is compared with the Genetic Algorithm (GA). The computational results show that the proposed PSO effectively outperforms GA especially in large-scale problems.     

三段式粒子群算法在车辆调度问题中的应用

针对标准粒子群算法在解决车辆调度问题上的不足,提出了一种基于整数编码的粒子群优化策略。它依据粒子群算法中粒子进化的思想,给出了三段式保优方法,重新定义了粒子进化速度和位置更新的方式。结合Floyd算法对调度模型进行了仿真验证分析。结果表明,该策略具有较好的寻优能力。     

粒群优化算法

本文首先简要描述了粒群算法一般形式,然后讨论了该算法的应用、发展和展望以及改进优化,然后介绍了一种基于压缩空间的CSV2PSO算法,给出了该算法的详细介绍以及和其它粒群算法的数值比较分析,它提高了粒群算法的收敛速度和收敛精度,降低了早熟收敛的比率,具有广阔的应用前景.     

电力系统无功优化中PSO算法的改进研究

针对电力系统无功优化中的PSO算法的特点,采用的信息拓扑结构为环形结构,对PSO算法中的变异算子进行研究.针对环形拓扑结构的PSO算法,其后期收敛精度差是一个常见问题,提出了一种称之为"球面变异"的变异算子,充分利用粒子群迭代后期种群的信息,对变异的方向与速度进行引导,进而建立了变异算子与当代种群适应度之间的关系,明显地提高了算法收敛速度与精度.最后,对陷入局部收敛等问题进行相应的改良,诸如无法达到最优解等问题.使用IEEE14节点系统作为算例进行测试,结果达到优良.     

粒子群优化点匹配算法

点匹配问题一直是计算机视觉,模式识别,医学临床诊断等领域的一项重要基础性工作。本文提出了一种基于粒子群优化算法的准确、快速和鲁棒性的点匹配方法。该方法首先确定两个特征点集的点匹配问题的能量函数,通过最小化该能量函数可以同时得到点集之间的匹配矩阵和映射参数,利用粒子群优化算法求解变换参数。实验表明,该算法适用于点匹配,具有操作方便,可靠性好,不易陷入局部极值等优点。     

An efficient fault-tolerant multicast routing protocol withcore-based tree techniques

In this paper, we design and analyze an efficient fault-tolerant multicast routing protocol. Reliable multicast communication is critical for the success of many Internet applications. Multicast routing protocols with core-based tree techniques (CBT) have been widely used because of their scalability and simplicity. We enhance the CBT protocol with fault tolerance capability and improve its efficiency and effectiveness. With our strategy, when a faulty component is detected, some pre-defined backup path(s) is (are) used to bypass the faulty component and enable the multicast communication to continue. Our protocol only requires that routers near the faulty component be reconfigured, thus reducing the runtime overhead without compromising much of the performance. Our approach is in contrast to other approaches that often require relatively large tree reformation when faults occur. These global methods are usually costly and complicated in their attempt to achieve theoretically optimal performance. Our performance evaluation shows that our new protocol performs nearly as well as the best possible global method while utilizing much less runtime overhead and implementation cost     

多任务处理协同进化粒子群算法

粒子群算法及其改进算法专注于单任务的求解.随着电子商务的发展,在线服务器在某一时刻需要同时处理来自多个用户的服务请求,即多任务处理.区别于以往的并行计算机,文中充分挖掘粒子群算法的“隐并行性”,并引入协同进化机制,在同一搜索空间根据任务个数设置不同的子种群,各子种群以一定的概率相互传递有效信息,最后提出基于多任务处理协同进化粒子群算法(CPSOM),并将CPSOM应用于多任务连续型函数优化问题、多任务离散型属性选择问题以及多任务约束工程优化问题.仿真实验表明,在CPSOM多任务环境中,不同任务之间确实存在有效信息的传递,不同任务之间的相互协作不仅可以提高解的质量,而且可以加快各优化问题的收敛速度.     

Simplifying Particle Swarm Optimization

The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, whereas previous approaches to meta-optimization have tuned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimizing a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases.     

微粒群优化算法

介绍了微粒群优化（PSO）算法的原理、算法流程、算法参数及其对算法性能的影响．讨论了各种改进的PSO算法．分析了多相微粒群优化算法（MPPSO）的原理、算法方程、算法参数及其对算法性能的影响．最后归纳了PSO算法的应用概况，并就PSO算法进一步的研究工作进行了探讨和展望．     

多极小值粒子群优化算法

针对标准粒子群算法只能搜索到目标函数一个最小值的缺点,提出多极小值粒子群算法.该算法通过在每一代粒子群中搜索极小值粒子,使得该算法中的粒子不仅具有目标函数的最小值点信息,而且还具有目标函数的极小值点信息,从而达到搜索目标函数最小值和多个极小值的目的.该算法消除了标准粒子群算法在搜索多极小值函数时全局最优粒子在不同极小值位置附近振荡的缺点,明显的提高了收敛的速率和搜索的精度.通过对典型的一维、二维和多维目标函数进行测试,证明了多极小值粒子群算法能够寻找到目标函数的全部极小值和其所在位置,且具有很强的全局收敛能力,验证了多极小值粒子群算法的有效性.     

非线性粒子群算法

提出了一种新型的粒子群算法一非线性粒子群算法,给出了计算公式并进行了实验模拟.非线性粒子群算法采用非线性计算公式调整粒子速度.由于非线性计算公式的多样性,因此可以构建种类繁多的具体的非线性粒子群算法.非线性粒子群算法一方面保持了标准粒子群算法的简单性,同时也具有更强的搜索能力.实际计算表明,只要能够选好非线性项中的参数,就可以提高算法的效率.     

组织进化粒子群数值优化算法

为充分利用粒子的通讯、响应、协作和自学习能力等特性,克服算法早熟收敛,本文提出一种组织进化粒子群算法.该算法将进化操作直接作用在组织上,通过组织间的相互竞争、协作,最终达到全局优化的目的,且证明算法的全局收敛性.实验中,用12个无约束标准测试函数对算法性能进行测试,与其它算法进行比较,并对算法中的参数进行分析.结果表明,本文算法无论在解的质量上还是在计算复杂度上都明显优于其它算法.参数分析表明该算法具有性能稳定、成功率高、对参数不敏感等优良特性.     

多目标优化的一种改进微粒群算法

微粒群算法是解决多目标优化问题的一个重要方法.为了多目标目标优化求解问题,常用的微粒群算法在处理多目标优化问题时,存在所得Pareto最优解集的分散性和实用性较差的缺点.针对上述问题,提出了微粒群算法的一种改进形式.改进算法引入了个体精英解集,从中选择更合适的个体最优位置.同时,在评价个体适应度时,考虑了目标函数值差异这一信息.个体对应的目标函数值差异大,则其适应度就小.这样能避免各目标函数值差异过大的最优解存在.三个典型的多目标测试函数表明,改进方法得到最优解集具有更好的分散性和实用性.测得结果证明,改进方法是有效的.