Optimal power flow of HVDC system using teaching–learning-based optimization algorithm

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.

     

含离散和连续混合决策变量最优潮流求解方法综述

最优潮流问题是一个含有连续变量和离散变量的非凸的、大规模的非线性规划问题,是混合整数非线性规划问题（MINLP）.它属于NP-hard问题,精确求解非常困难.本文对含离散和连续混合决策变量最优潮流问题的求解算法进行了分类和总结.介绍了各种求解技术的原理和具体做法,并从算法的收敛性、准确性、快速性等角度对它们进行了评价,指出了它们各自的优缺点及应用价值.     

A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs

This work deals with a class of problems under interval data uncertainty, namely interval robust-hard problems, composed of interval data min-max regret generalizations of classical NP-hard combinatorial problems modeled as 0-1 integer linear programming problems. These problems are more challenging than other interval data min-max regret problems, as solely computing the cost of any feasible solution requires solving an instance of an NP-hard problem. The state-of-the-art exact algorithms in the literature are based on the generation of a possibly exponential number of cuts. As each cut separation involves the resolution of an NP-hard classical optimization problem, the size of the instances that can be solved efficiently is relatively small. To smooth this issue, we present a modeling technique for interval robust-hard problems in the context of a heuristic framework. The heuristic obtains feasible solutions by exploring dual information of a linearly relaxed model associated with the classical optimization problem counterpart. Computational experiments for interval data min-max regret versions of the restricted shortest path problem and the set covering problem show that our heuristic is able to find optimal or near-optimal solutions and also improves the primal bounds obtained by a state-of-the-art exact algorithm and a 2-approximation procedure for interval data min-max regret problems.     

A survey of distributed optimization

In distributed optimization of multi-agent systems, agents cooperate to minimize a global function which is a sum of local objective functions. Motivated by applications including power systems, sensor networks, smart buildings, and smart manufacturing, various distributed optimization algorithms have been developed. In these algorithms, each agent performs local computation based on its own information and information received from its neighboring agents through the underlying communication network, so that the optimization problem can be solved in a distributed manner. This survey paper aims to offer a detailed overview of existing distributed optimization algorithms and their applications in power systems. More specifically, we first review discrete-time and continuous-time distributed optimization algorithms for undirected graphs. We then discuss how to extend these algorithms in various directions to handle more realistic scenarios. Finally, we focus on the application of distributed optimization in the optimal coordination of distributed energy resources.     

概率图模型推理方法的研究进展

近年来概率图模型已成为不确定性推理的研究热点,在人工智能、机器学习与计算机视觉等领域有广阔的应用前景.根据网络结构与查询问题类型的不同,系统地综述了概率图模型的推理算法.首先讨论了贝叶斯网络与马尔可夫网络中解决概率查询问题的精确推理算法与近似推理算法,其中主要介绍精确推理中的VE算法、递归约束算法和团树算法,以及近似推理中的变分近似推理和抽样近似推理算法,并给出了解决MAP查询问题的常用推理算法;然后分别针对混合网络的连续与混合情况阐述其推理算法,并分析了暂态网络的精确推理、近似推理以及混合情况下的推理;最后指出了概率图模型推理方法未来的研究方向.     

Improving probabilistic inference in graphical models with determinism and cycles

Many important real-world applications of machine learning, statistical physics, constraint programming and information theory can be formulated using graphical models that involve determinism and cycles. Accurate and efficient inference and training of such graphical models remains a key challenge. Markov logic networks (MLNs) have recently emerged as a popular framework for expressing a number of problems which exhibit these properties. While loopy belief propagation (LBP) can be an effective solution in some cases; unfortunately, when both determinism and cycles are present, LBP frequently fails to converge or converges to inaccurate results. As such, sampling based algorithms have been found to be more effective and are more popular for general inference tasks in MLNs. In this paper, we introduce Generalized arc-consistency Expectation Maximization Message-Passing (GEM-MP), a novel message-passing approach to inference in an extended factor graph that combines constraint programming techniques with variational methods. We focus our experiments on Markov logic and Ising models but the method is applicable to graphical models in general. In contrast to LBP, GEM-MP formulates the message-passing structure as steps of variational expectation maximization. Moreover, in the algorithm we leverage the local structures in the factor graph by using generalized arc consistency when performing a variational mean-field approximation. Thus each such update increases a lower bound on the model evidence. Our experiments on Ising grids, entity resolution and link prediction problems demonstrate the accuracy and convergence of GEM-MP over existing state-of-the-art inference algorithms such as MC-SAT, LBP, and Gibbs sampling, as well as convergent message passing algorithms such as the concave–convex procedure, residual BP, and the L2-convex method.     

Heterogeneous computing scheduling with evolutionary algorithms

This work presents sequential and parallel evolutionary algorithms (EAs) applied to the scheduling problem in heterogeneous computing environments, a NP-hard problem with capital relevance in distributed computing. These methods have been specifically designed to provide accurate and efficient solutions by using simple operators that allow them to be later extended for solving realistic problem instances arising in distributed heterogeneous computing (HC) and grid systems. The EAs were codified over MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on well-known problem instances. The comparative study of scheduling methods shows that the parallel versions of the implemented evolutionary algorithms are able to achieve high problem solving efficacy, outperforming traditional scheduling heuristics and also improving over previous results already reported in the related literature.     

Resource finding in store-and-forward networks

We present a model of searching for a resource in a distributed system whose nodes are connected through a store-and-forward network. Based on this model, we show a lower bound on the number of messages needed to find a resource when nothing is known about the nodes that have the current location of the resource. The model also helps us to establish results about the time complexity of determining a message optimal resource finding algorithm when the probability distribution for the location of the resource in the network is known. We show that the optimization problem is NP-hard for general networks. Finally we show that optimal resource finding algorithms can be determined in polynomial time for a class of tree networks and bidirectional rings. The polynomial algorithms can be used as a basis of heuristic algorithms for general networks.This work was supported in part by NSF grants CCR-8806358 and NCR-8604850     

Graphical model construction based on evolutionary algorithms

1 Introduction Evolutionary algorithms(EAs) [1～5] are stochastic search and optimization techniques, which were inspired by the analogy of evolution and population genetics. They have been demonstrated to be effective and robust in searching very large, varied, spaces in a wide range of applications, including classification, machine learning, ecological, so- cial systems and so on. However, most of the common evo- lutionary algorithms using simple operators are incapable of learning the reg…     

概率图模型学习技术研究进展

概率图模型能有效处理不确定性推理,从样本数据中准确高效地学习概率图模型是其在实际应用中的关键问题.概率图模型的表示由参数和结构两部分组成,其学习算法也相应分为参数学习与结构学习.本文详细介绍了基于概率图模型网络的参数学习与结构学习算法,并根据数据集是否完备而分别讨论各种情况下的参数学习算法,还针对结构学习算法特点的不同把结构学习算法归纳为基于约束的学习、基于评分搜索的学习、混合学习、动态规划结构学习、模型平均结构学习和不完备数据集的结构学习.并总结了马尔科夫网络的参数学习与结构学习算法.最后指出了概率图模型学习的开放性问题以及进一步的研究方向.     

Ising图模型概率推理的计算复杂性

图模型概率推理的主要任务是通过对联合概率分布进行变量求和来计算配分函数、变量边缘概率分布、条件 概率分布等。图模型概率推理计算复杂性及近似概率推理的计算复杂性是一重要的理论问题,也是设计概率推理算 法和近似概率推理算法的理论基础。研究了Ising图模型概率推理的计算复杂性,包括概率推理的难解性及不可近似 性。具体地,通过构建#2 SA"I'问题到Icing图模型概率推理问题的多项式时间计数归约,证明在一般 Ising图模型上 计算配分函数、变量边缘概率分布、条件概率分布的概率推理问题是#P难的,同时证明Icing图模型近似概率推理问 题是NP难的,即一般Icing图模型上的概率推理问题是难解且不可近似的。     

Ising图模型概率推理的参数化复杂性

Ising图模型概率推理的主要工作是通过变量求和来计算配分函数和边缘概率分布。传统计算复杂性理论证明Ising图模型精确概率推理是NP难的,并且Ising图模型近似概率推理是NP难的。研究了Ising图模型精确概率推理和Ising均值场近似概率推理的参数化复杂性。首先证明了不同参数的Ising图模型概率推理的参数化复杂性定理,指出基于变量个数或图模型树宽的参数化概率推理问题是固定参数可处理的。然后证明了Ising均值场的参数化复杂性定理,指出基于自由分布树宽、迭代次数和变量个数的参数化Icing均值场是固定参数可处理的;进一步,当Ising图模型参数满足Ising均值场迭代式压缩条件时,基于自由分布树宽和迭代次数的参数化Ising均值场是固定参数可处理的。     

Graphical model construction based on evolutionary algorithms

Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well.     

Gibbs sampling based distributed OFDMA resource allocation

In this article, we present a distributed resource and power allocation scheme for muRip]e-resource wireless cellular networks. The global optimization of multi-cell multi-link resource allocation problem is known to be NP-hard in the general case. We use Gibbs sampling based algorithms to perform a distributed optimization that would lead to the global optimum of the problem. The objective of this article is to show how to use the Gibbs sampling （GS） algorithm and its variant the Metropolis-Hastings （MH） algorithm. We also propose an enhanced method of the MH algorithm, based on a priori known target state distribution, which improves the convergence speed without increasing the complexity. Also, we study different temperature cooling strategies and investigate their impact on the network optimization and convergence speed. Simulation results have also shown the effectiveness of the proposed methods.     

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Load balanced transaction scheduling problem is an important issue in distributed computing environments including grid system. This problem is known to be NP-hard and can be solved by using heuristic as well as any meta-heuristic method. We ponder over the problem of the load balanced transaction scheduling in a grid processing system by using an Ant Colony Optimization for load balancing. The problem that we consider is to achieve good execution characteristics for a given set of transactions that has to be completed within their given deadline. We propose a transaction processing algorithm based on Ant Colony Optimization (ACO) for load balanced transaction scheduling. We modify two meta-heuristic along with ACO and three heuristic scheduling algorithms for the purpose of comparison with our proposed algorithm. The results of the comparison show that the proposed algorithm provides better results for the load balanced transaction scheduling in the grid processing system.     

Energy efficient multicast routing in ad hoc wireless networks

In this paper, we discuss the energy efficient multicast problem in ad hoc wireless networks. Each node in the network is assumed to have a fixed level of transmission power. The problem of our concern is: given an ad hoc wireless network and a multicast request, how to find a multicast tree such that the total energy cost of the multicast tree is minimized. We first prove this problem is NP-hard and it is unlikely to have an approximation algorithm with a constant performance ratio of the number of nodes in the network. We then propose an algorithm based on the directed Steiner tree method that has a theoretically guaranteed approximation performance ratio. We also propose two efficient heuristics, node-join-tree (NJT) and tree-join-tree (TJT) algorithms. The NJT algorithm can be easily implemented in a distributed fashion. Extensive simulations have been conducted to compare with other methods and the results have shown significant improvement on energy efficiency of the proposed algorithms.     

GSST: anytime guaranteed search

We present Guaranteed Search with Spanning Trees (GSST), an anytime algorithm for multi-robot search. The problem is as follows: clear the environment of any adversarial target using the fewest number of searchers. This problem is NP-hard on arbitrary graphs but can be solved in linear-time on trees. Our algorithm generates spanning trees of a graphical representation of the environment to guide the search. At any time, spanning tree generation can be stopped yielding the best strategy so far. The resulting strategy can be modified online if additional information becomes available. Though GSST does not have performance guarantees after its first iteration, we prove that several variations will find an optimal solution given sufficient runtime. We test GSST in simulation and on a human-robot search team using a distributed implementation. GSST quickly generates clearing schedules with as few as 50% of the searchers used by competing algorithms.     

无线多跳网络中吞吐量优化的多播路由算法研究*

现有网络中提高多播吞吐量的算法通常是以提高链路速率为目的,但单纯地提高链路速率而忽略多播树的度也限制了多播吞吐量的提高。主要研究了多跳无线网络中多播吞吐量最优化问题,深入分析了无线多跳网络特点,并在综合考虑链路速率和多播树度对多播吞吐量影响的基础上,提出了应用于节点发射功率相同环境下的UUP_MTOA算法和应用于节点发射功率不同环境下的UNP_MTOA算法。通过仿真实验与同类近似最优化算法相比,UUP_MTOA算法和UNP_MTOA算法能够获得更高的吞吐量,更适应于多跳无线网络环境。     

路径和下界公式及其在调度问题中的应用

主要是研究以makespan为目标的多机器置换flow shop调度问题.由于它NP-困难,无法用多项式时间算法求解.而路径和关键路径是研究该问题的一个有力工具,对其进行深入分析可以帮助进一步理解问题最基本的特?挖掘其在更多优化算法中的应用,增加该算法的效率.在研究了原有的关键路径基础上,借助于有向栅格图和重新定义的路径及关键路径公式,直观地给出了对工件排列进行插入移动后的新排列的下界公式.另外,在遗传算法和禁忌搜索之外,还给出了下界公式在NEH启发式算法中的应用,并通过实验验证了它在排除没有希望的排列、减少有效搜索空间的强大能力.     

有向传感器网络中基于概率感知模型的最小连通k覆盖集算法

无线传感器网络的基本问题之一是,网络节点如何利用有限的能量对人们所关注的物理世界进行满意的监测,这可抽象为最小连通k覆盖集问题。传统的最小连通k覆盖集问题是基于确定型全向感知模型的,该模型过于理想化,不能适用于复杂的应用环境,也不能应用于有向传感器网络中。针对上述局限,本文提出了有向传感器网络中基于概率感感知模型的最小连通k覆盖集问题（MCKS）,并指出这是NP难问题;设计了基于0-1整数规划和最小生成树的集中式近 BDA）,分别证明两种算法最终得到的是MCKS问题的可行解,并分析了算法的时间复杂度、性能比和通信复杂度。通过仿真实验并与ILP算法和BGA算法进行比较的结果表明： 在基于概率感知模型的条件下,IPA和CBDA能够有效实现有向传感器网络中的连通k覆盖,并且激活节点数目较少,网络寿命延长。