Parallel mesh-partitioning algorithms for generating shape optimised partitions using evolutionary computing

In this paper, parallel mesh-partitioning algorithms are proposed for generating submeshes with optimal shape using evolutionary computing techniques. It is preferred to employ a formulation for mesh partitioning, which maintains constant number of design variables irrespective of the size of the mesh. Two distinct parallel computing models have been employed. The first model of parallel evolutionary algorithm uses the master–slave concept (single population model) and a new synchronous model is proposed to optimise the performance even on heterogeneous parallel hardware. Alternatively, a multiple population model is also developed which simulates it’s sequential counter part. The advantage of the second model is that it can fit in large size problems with large population even on moderate capacity parallel computing nodes. The performance of the evolutionary computing based mesh-partitioning algorithm is demonstrated first by solving several practical engineering problems and also several benchmark test problems available in the literature and comparing the results with the multilevel algorithms. Later the speedup of the parallel evolutionary algorithms on parallel hardware is evaluated by solving large scale practical engineering problems.     

Scale factor inheritance mechanism in distributed differential evolution

This article proposes a distributed differential evolution which employs a novel self-adaptive scheme, namely scale factor inheritance. In the proposed algorithm, the population is distributed over several sub-populations allocated according to a ring topology. Each sub-population is characterized by its own scale factor value. With a probabilistic criterion, that individual displaying the best performance is migrated to the neighbor population and replaces a pseudo-randomly selected individual of the target sub-population. The target sub-population inherits not only this individual but also the scale factor if it seems promising at the current stage of evolution. In addition, a perturbation mechanism enhances the exploration feature of the algorithm. The proposed algorithm has been run on a set of various test problems and then compared to two sequential differential evolution algorithms and three distributed differential evolution algorithms recently proposed in literature and representing state-of-the-art in the field. Numerical results show that the proposed approach seems very efficient for most of the analyzed problems, and outperforms all other algorithms considered in this study.     

A new dandelion algorithm and optimization for extreme learning machine

In this paper, we proposed the Dandelion Algorithm (DA), based on the behaviour of dandelion sowing. In DA, the dandelion is sown in a certain range based on dynamic radius. Meanwhile the dandelion has self-learning ability; it could select a number of excellent seeds to learn. We compare the proposed algorithm with other existing algorithms. Simulations show that the proposed algorithm seems much superior to other algorithms. Moreover, the proposed algorithm can be applied to optimise extreme learning machine (ELM), which has a very good classification and prediction capability.     

A general packing algorithm for multidimensional resource requirements

Many problems in resource allocations, memory allocation, and distributed computer system design can be formulated as problems of packing variablesized items into fixed-sized containers in order to minimize the total number of containers used. In this paper, a generalized packing algorithm that encompasses many well-known heuristic packing algorithms is proposed. Simulation results on this generalized packing algorithm are described, and their implications are discussed. The objective of this paper is to investigate the performance of various heuristic packing algorithms within a general framework, to obtain numerical estimates on their efficiency, and to provide guidelines on the use of these algorithms.     

Distributed cooperative learning algorithms using wavelet neural network

This paper investigates the distributed cooperative learning (DCL) problems over networks, where each node only has access to its own data generated by the unknown pattern (map or function) uniformly, and all nodes cooperatively learn the pattern by exchanging local information with their neighboring nodes. These problems cannot be solved by using traditional centralized algorithms. To solve these problems, two novel DCL algorithms using wavelet neural networks are proposed, including continuous-time DCL (CT-DCL) algorithm and discrete-time DCL (DT-DCL) algorithm. Combining the characteristics of neural networks with the properties of the wavelet approximation, the wavelet series are used to approximate the unknown pattern. The DCL algorithms are used to train the optimal weight coefficient matrix of wavelet series. Moreover, the convergence of the proposed algorithms is guaranteed by using the Lyapunov method. Compared with existing distributed optimization strategies such as distributed average consensus (DAC) and alternating direction method of multipliers (ADMM), our DT-DCL algorithm requires less information communications and training time than ADMM strategy. In addition, it achieves higher accuracy than DAC strategy when the network consists of large amounts of nodes. Moreover, the proposed CT-DCL algorithm using a proper step size is more accurate than the DT-DCL algorithm if the training time is not considered. Several illustrative examples are presented to show the efficiencies and advantages of the proposed algorithms.

     

基于连续时间的二阶多智能体分布式资源分配算法

针对二阶多智能体系统中的分布式资源分配问题, 本文设计两种连续时间算法. 基于KKT (Karush?Kuhn?Tucker, 卡罗需?库恩?塔克)优化条件, 第一种控制算法利用节点局部不等式及其梯度信息来约束节点状态. 与上述梯度方法不同, 第二种控制算法包括一致性梯度下降法和固定时间收敛映射算子, 其中固定时间收敛映射算子确保算法的节点状态在固定时间收敛到局部约束集, 一致性梯度下降法目的是确保节点迭代到资源分配问题最优解. 两种控制算法都对状态无初始值约束, 且控制参数都是常数. 利用凸优化理论和固定时间李雅普诺夫方法, 分别分析了上述控制策略在有向平衡网络条件下的渐近和指数收敛性. 最后通过数值仿真验证了所设计算法在一维和高维资源分配问题的有效性.     

dPageRank--一种改进的分布式PageRank算法

回顾了传统的PageRank计算方式,分析了等级泄漏和悬挂页面问题的解决方法。介绍了分布式PageRank的计算原理和评价原则,在分析两种现有分布式PageRank算法的基础上,提出了一种改进的分布式PageRank算法,通过实验对该算法的性能进行分析评价。     

Many-objective evolutionary algorithm based on adaptive weighted decomposition

Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts.     

图形着色问题的分布式势博弈算法

现有典型的分布式算法在解决大规模图形着色问题时,必须维持节点间的通信连接,在邻接节点增长时效率和可求解规模下降明显。为此,将多代理技术平台下的图像着色问题转换为博弈模型,采用自适应学习算法,逐步优化代理自身状态行为以达到系统的最优状态,即纳什均衡点。实验结果表明,较现有的分布式算法,该算法不但具有更高的求解效率,能够解决更大规模的图形着色问题,而且对邻接节点规模变化的适应能力进一步提高。     

基于事件触发的分布式优化算法

本文研究了一类分布式优化问题,其目标是通过局部信息交换使由局部成本函数之和构成的全局成本函数最小.针对无向连通图,我们提出了两种基于比例积分策略的分布式优化算法.在局部成本函数可微且凸的条件下,证明了所提算法渐近收敛到全局最小值点.更进一步,在局部成本函数具有局部Lipschitz梯度和全局成本函数关于全局最小值点是有...     

Multimedia resource allocation strategy of wireless sensor networks using distributed heuristic algorithm in cloud computing environment

The virtualized resource allocation (mapping) algorithm is the core issue of network virtualization technology. Universal and excellent resource allocation algorithms not only provide efficient and reliable network resources sharing for systems and users, but also simplify the complexity of resource scheduling and management, improve the utilization of basic resources, balance network load and optimize network performance. Based on the application of wireless sensor network, this paper proposes a wireless sensor network architecture based on cloud computing. The WSN hardware resources are mapped into resources in cloud computing through virtualization technology, and the resource allocation strategy of the network architecture is proposed. The experiment evaluates the performance of the resource allocation strategy. The proposed heuristic algorithm is a distributed algorithm. The complexity of centralized algorithms is high, distributed algorithms can handle problems in parallel, and reduce the time required to get a good solution with limited traffic.

     

面向卫星网络的延迟监测算法及仿真实现

研究测量分析网络内部链路延迟特征对了解网络行为至关重要,但当前地面网络延迟监测算法应用于卫星网络时存在诸多不准确问题,为此提出了一种面向卫星网络的延迟监测算法,它结合主动监测和被动监测的优点,采用统计分析方法测量卫星网络系统的报文流延迟分布特征。通过理论分析证明了算法的数学基础,并在网络仿真软件NS2下通过高、低轨道卫星网络模型对算法进行了仿真实验,实验结果表明,算法能够在较小的开销下解决卫星网络的延迟推测问题。     

面向云制造平台的模型修复算法及任务调度算法的研究

随着三维打印技术的发展,面向3D打印服务的云制造平台也得到快速发展,改变着传统的制造模式。然而目前的云制造平台也面临着诸多问题,针对目前存在的模型修复难度高及任务不能合理调度的问题,本文提出了保特征的模型修复算法和基于遗传算法的任务调度算法,并通过实验和仿真验证了算法的有效性。为了更好的体现算法效果,本文搭建了一个基于分布式制造的云制造平台,该平台配合模型自动修复算法以及基于遗传算法的任务调度算法,为用户提供低门槛、高效、优质的3D打印服务。     

Input feature selection for classification problems

Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.     

Methods of resource management in problem-oriented computing environment

One of the important classes of computational problems is problem-oriented workflow applications executed in distributed computing environment. A problem-oriented workflow application can be represented by a directed graph whose vertices are tasks and arcs are data flows. For a problem-oriented workflow application, we can get a priori estimates of the task execution time and the amount of data to be transferred between the tasks. A distributed computing environment designed for the execution of such tasks in a certain subject domain is called problem-oriented environment. To efficiently use resources of the distributed computing environment, special scheduling algorithms are applied. Nowadays, a great number of such algorithms have been proposed. Some of them (like the DSC algorithm) take into account specific features of problem-oriented workflow applications. Others (like Min–Min algorithm) take into account many-core structure of nodes of the computational network. However, none of them takes into account both factors. In this paper, a mathematical model of problem-oriented computing environment is constructed, and a new problem-oriented scheduling (POS) algorithm is proposed. The POS algorithm takes into account both specifics of the problem-oriented jobs and multi-core structure of the computing system nodes. Results of computational experiments comparing the POS algorithm with other known scheduling algorithms are presented.     

异构系统中基于可用性的粒子群任务调度算法

任务调度技术是并行分布式系统中的关键技术之一,对系统的性能起着重要作用,但通常情况下大型系统的任务调度问题属于NP问题。而现代启发式生物进化算法是找出很多NP问题近似解的有效方法。本文将粒子群算法应用于基于可用性的网格系统调度中,提出了一种调度算法,对算法的性能进行了理论分析和模拟实验。结果表明:和最近文献中的基于可用性的调度算法SSAC相比,所提出的新算法在保证系统资源具有同样的可用性条件下,能够产生更好的调度长度。     

Distributed primal–dual interior-point methods for solving tree-structured coupled convex problems using message-passing

In this paper, we propose a distributed algorithm for solving coupled problems with chordal sparsity or an inherent tree structure which relies on primal–dual interior-point methods. We achieve this by distributing the computations at each iteration, using message-passing. In comparison to existing distributed algorithms for solving such problems, this algorithm requires far fewer iterations to converge to a solution with high accuracy. Furthermore, it is possible to compute an upper-bound for the number of required iterations which, unlike existing methods, only depends on the coupling structure in the problem. We illustrate the performance of our proposed method using a set of numerical examples.     

Distributed optimal resource allocation of second‐order multiagent systems

In this paper, the resource allocation problems of multiagent systems are investigated. Different from the well‐studied resource allocation problems, the dynamics of agents are taken into account in our problem, which results that the problem could not be solved by most of existing resource allocation algorithms. Here, the agents are in the form of second‐order dynamics, which causes the difficulties in designing and analyzing distributed resource allocation algorithms. Based on gradient descent and state feedback, two distributed resource allocation algorithms are proposed to achieve the optimal allocation, and their convergence are analyzed by constructing suitable Lyapunov functions. One of the two algorithms can ensure that the decisions of all agents asymptotically converge to the exact optimal solution, and the other algorithm achieves the exponential convergence. Finally, numerical examples about the economic dispatch problems of power grids are given to verify the effectiveness of the obtained results.     

基于梯度估计的多智能体系统有限时间分布式优化

现有多智能体系统分布式优化算法大多具有渐近收敛速度,且要求系统的网络拓扑图为无向图或有向平衡图,在实际应用中具有一定的保守性.本文研究了具有强连通拓扑的多智能体系统有限时间分布式优化问题.首先,基于非光滑分析和Lyapunov稳定性理论设计了一个有限时间分布式梯度估计器.然后,基于该梯度估计器提出了一种适用于强连通有向图的有限时间分布式优化算法,实现了多智能体系统中智能体的状态在有限时间内一致收敛到全局最优状态值.与现有的有限时间分布式优化算法相比,新提出的有限时间优化算法适用于具有强连通拓扑的多智能体系统,放宽了系统对网络拓扑结构的要求.此外,本文基于Nussbaum函数方法对上述优化算法进行了拓展解决了含有未知高频增益符号的多智能体系统分布式优化问题.最后,通过仿真实例对提出的分布式优化算法的有效性进行了验证.     

基于新模型的多目标Memetic算法及收敛分析

将多目标函数优化问题转化成单目标约束优化问题.对转化后的问题提出了基于约束主导原理的选择方法,克服了多数方法只使用Pareto优胜关系作为选择策略而没有采用偏好信息这一缺陷;Memetic算法是求解多目标优化问题最有效的方法之一,它融合了局部搜索和进化计算.新的多目标Memetic算法引进C-metric,将模拟退火算法与遗传算法结合起米,改善了全局搜索能力.用概率论的有关知识证明了算法的收敛性.仿真结果表明该方法对不同的试验函数均可求出一组沿着Pareto前沿分布均匀且散布广泛的非劣解.