量子粒子群算法求解QoS组播路由

QoS组播路由问题是一个非线性的组合优化问题,已证明了该问题是NP完全问题。将量子粒子群算法用于此类问题的求解。并在此基础上对基本的量子粒子群算法进行改进,针对群体智能和约束优化问题的特点,提出了一种在每次迭代中有选择地保留一定数量不可行解的方法,并把它结合到量子粒子群优化（QDPSO）算法中。该算法可以利用保留下来的不可行解来帮助搜索靠近边界的最优解,同时又可以避免罚因子的选择问题,使之更适合于QoS组播路由的求解。仿真实验结果显示,该算法能快速搜索并收敛到全局（近似）最优解,且随着网络规模的增大算法保持了良好的特性,在寻优速度上与解的质量上优于其他粒子群算法与基本的量子粒子群算法。     

微粒群优化的应用层组播路由算法

作为一种基于应用层的多用户数据共享方案,应用层组播在互联网中的应用日益广泛。然而目前应用层组播仍然面临着延迟过大、终端负载过重等问题。针对应用层组播的路由转发特征,将应用层组播问题抽象为度和延迟约束的最小生成树问题,进而提出了一种新的基于微粒群优化（Particle Swarm Optimization,PSO）的应用层组播路由算法。仿真实验表明,算法有着良好的扩展性和较高的效率。     

基于QPSO的QoS组播路由算法

对带宽、延时、延时抖动约束最小代价的QoS组播路由问题进行了研究，提出一种基于量子行为微粒群优化（QPSO）算法来设计路由优化算法。该算法采用一种节点序列编码方案，将路由优化问题转化成一种准连续优化问题，并采用罚函数处理约束条件。应用QPSO算法求解QoS组播路由问题的算例，并与遗传算法和改进后的遗传算法进行比较。计算机仿真实验证明，该算法可以更有效地求得QoS组播路由问题的优化解，可靠性较高。     

SoMR: A scalable distributed QoS multicast routing protocol

Many Internet multicast applications such as teleconferencing and remote diagnosis have Quality-of-Service (QoS) requirements. The requirements can be additive (end-to-end delay), multiplicative (loss rate), or of a bottleneck nature (bandwidth). Given such diverse requirements, it is a challenging task to build QoS-constrained multicast trees in a large network where no global network state is available. This paper proposes a scalable QoS multicast routing protocol (SoMR) that supports all three QoS requirement types. SoMR is scalable due to small communication overhead. It achieves favorable tradeoff between routing performance and routing overhead by carefully selecting the network sub-graph in which it searches for a path that can support the QoS requirements. The scope of search is automatically tuned based on the current network conditions. An early-warning mechanism helps detect and route around the long-delay paths in the network. The operations of SoMR are completely decentralized. They rely only on the local state stored at each router.     

粒子群优化ABC支持型QoS单播路由机制

设计了一种ABC支持型QoS单播路由机制,采用区间形式描述不精确用户QoS需求和边（链路）参数,引入用户满意度、边评价和精英解集,通过博弈分析,应用粒子群优化算法,寻找使用户和网络提供方效用达到或接近Nash均衡下Pareto最优的QoS单播路径。仿真结果表明,该机制是可行和有效的。     

自适应免疫量子粒子群优化并行算法

提出了自适应免疫量子粒子群优化并行算法。为了克服粒子群优化算法早熟收敛以及粒子在进化过程中缺乏很好的方向指导的问题,采用了量子技术以及免疫机制,从而获得了一个自适应免疫量子粒子群优化算法。同时,针对该算法计算量大、耗时长的缺点,结合已有的并行计算技术,构造出了该算法的并行计算方法。仿真实验表明所提并行算法具有较好的性能。     

Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers’ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers’ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

Accurate sub-swarms particle swarm optimization algorithm for service composition

Service composition (SC) generates various composite applications quickly by using a novel service interaction model. Before composing services together, the most important thing is to find optimal candidate service instances compliant with non-functional requirements. Particle swarm optimization (PSO) is known as an effective and efficient algorithm, which is widely used in this process. However, the premature convergence and diversity loss of PSO always results in suboptimal solutions. In this paper, we propose an accurate sub-swarms particle swarm optimization (ASPSO) algorithm by adopting parallel and serial niching techniques. The ASPSO algorithm locates optimal solutions by using sub-swarms searching grid cells in which the density of feasible solutions is high. Simulation results demonstrate that the proposed algorithm improves the accuracy of the standard PSO algorithm in searching the optimal solution of service selection problem.     

量子行为粒子群优化算法的布局问题研究

基于多边形扫描转换的启发式底左（HBL）算法,将量子行为的粒子群算法（QPSO）应用于布局问题——二维不规则多边形优化排样,给出了该问题的粒子构造方法及其布局优化过程。通过与模拟退火遗传算法（SAGA）进行布局优化比较,验证了该算法求解布局优化问题的有效性。     

一种弹性粒子群优化算法

当某个粒子与最优粒子很接近时,其飞行速度将趋于零,这是粒子群优化算法容易陷入局部极小的主要原因.为此,提出一种弹性粒子群优化算法.算法中,粒子速度不依赖其与最优粒子之间距离的大小,而仅依赖于其方向信息,并采用一种自适应策略弹性地修正粒子速度的幅值.将弹性粒子群优化算法应用于几种典型测试函数的优化,数值仿真结果表明,弹性粒子群优化算法能有效地找出全局最优点.     

群智能在Ad Hoc网络QoS组播路由中应用的研究

通过对Ad Hoc网络QoS组播路由问题的深入研究,提出了一种融合量子粒子群优化和蚁群优化的群智能混合算法(QPSOACO算法)。该算法融合QPSO思想以加速蚁群算法在路由发现及维护时的收敛速度,进一步提高算法对网络节点移动性的适应能力。仿真实验表明,该算法对Ad Hoc网络环境的适应性良好。     

具有学习行为的协同量子粒子群算法

提出了一种具有学习行为的协同量子粒子群算法(LCQPSO).针对量子粒子群(QPSO)存在的早熟收敛问题,从两方面对其进行改进:引入多子群协同搜索策略提高种群的全局搜索能力,使其在进化后期依然保持多样性;赋予粒子学习行为,提高种群的局部搜索能力.实验中对LCQPSO算法的子群规模与学习概率参数进行了分析,并利用标准测试函数对LCQPSO与PSO、QPSO等算法进行了比较测试,结果表明LCQPSO算法具有更优秀的收敛速度与精度,且能够有效地避免陷入局部极值.     

Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm

In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modeling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP.     

自学习的量子粒子群优化算法改进

分析了量子行为粒子群优化算法,着重研究了算法中群体粒子的搜索行为,对算法中局部吸引点进行了分析,提出针对粒子在搜索过程中所处的不同搜索环境,将粒子的搜索行为分为四种类型,并能够自适应地学习优化问题环境,采用合适的学习模式,提高算法整体优化性能;将改进后的自学习量子粒子群算法与其他一些改进方法通过CEC2005 benchmark测试函数进行了比较,最后对结果进行了分析,仿真结果显示自学习方法能够显著改善量子粒子群优化算法的性能。     

A modified particle swarm optimization for aggregate production planning

Particle swarm optimization (PSO) originated from bird flocking models. It has become a popular research field with many successful applications. In this paper, we present a scheme of an aggregate production planning (APP) from a manufacturer of gardening equipment. It is formulated as an integer linear programming model and optimized by PSO. During the course of optimizing the problem, we discovered that PSO had limited ability and unsatisfactory performance, especially a large constrained integral APP problem with plenty of equality constraints. In order to enhance its performance and alleviate the deficiencies to the problem solving, a modified PSO (MPSO) is proposed, which introduces the idea of sub-particles, a particular coding principle, and a modified operation procedure of particles to the update rules to regulate the search processes for a particle swarm. In the computational study, some instances of the APP problems are experimented and analyzed to evaluate the performance of the MPSO with standard PSO (SPSO) and genetic algorithm (GA). The experimental results demonstrate that the MPSO variant provides particular qualities in the aspects of accuracy, reliability, and convergence speed than SPSO and GA.     

一种多样性控制的粒子群优化算法

针对粒子群优化(PSO)算法的早熟收敛问题,提出一种新的基于群体多样性控制的PSO算法(DCPSO).该方法使得粒子在收缩状态下充分搜索,在发散状态下能够飞离群体的聚集位置,不断的收缩-发散过程保证了群体能在较大的空间进行搜索,减少了粒子群算法的早熟收敛现象.通过对多个标准测试函数的实验结果表明,DCPSO算法在复杂优化问题中具有较强的全局搜索能力,而且比现有的多样性指导的PSO算法(ARPSO)具有更好的性能.     

A perturbed particle swarm algorithm for numerical optimization

The canonical particle swarm optimization (PSO) has its own disadvantages, such as the high speed of convergence which often implies a rapid loss of diversity during the optimization process, which inevitably leads to undesirable premature convergence. In order to overcome the disadvantage of PSO, a perturbed particle swarm algorithm (pPSA) is presented based on the new particle updating strategy which is based upon the concept of perturbed global best to deal with the problem of premature convergence and diversity maintenance within the swarm. A linear model and a random model together with the initial max–min model are provided to understand and analyze the uncertainty of perturbed particle updating strategy. pPSA is validated using 12 standard test functions. The preliminary results indicate that pPSO performs much better than PSO both in quality of solutions and robustness and comparable with GCPSO. The experiments confirm us that the perturbed particle updating strategy is an encouraging strategy for stochastic heuristic algorithms and the max–min model is a promising model on the concept of possibility measure.     

具有量子行为粒子群优化算法的并行化研究

在研究了具有量子行为粒子群算法的基础上,受遗传算法并行化的启发,对具有量子行为的粒子群算法提出并实现了新的并行化策略。针对通信时间过长的问题,提出了改进方法。最后通过benchmark测试函数,将并行化量子粒子优化算法和二进制遗传算法、十进制遗传算法、粒子群优化算法的并行化方法进行了仿真比较,并对结果进行了分析。     

一种求解多峰函数优化问题的量子行为粒子群算法

介绍了一种利用量子行为粒子群算法（QPSO）求解多峰函数优化问题的方法。为此,在QPSO中引进一种物种形成策略,该方法根据群体微粒的相似度并行地分成子群体。每个子群体是围绕一个群体种子而建立的。对每个子群体通过QPSO算法进行最优搜索,从而保证每个峰值都有同等机会被找到,因此该方法具有良好的局部寻优特性。将基于物种形成的QPSO算法与粒子群算法(PSO)对多峰优化问题的结果进行比较。对几个重要的测试函数进行仿真实验结果证明,基于物种形成的QPSO算法可以尽可能多地找到峰值点,峰值收敛性能优于PSO。